Dynamic lane biasing

ABSTRACT

Techniques for generating trajectories and drivable areas for navigating a vehicle in an environment are discussed herein. The techniques can include receiving a reference trajectory representing an initial trajectory for a vehicle, such as an autonomous vehicle, to traverse the environment in a first drivable area. An object within a distance threshold can be detected in the environment and a second drivable area can be determined. Further, the techniques can include determining a target trajectory based at least in part on the reference trajectory and/or the second drivable area which can provide a region for the object to traverse the environment, and controlling the autonomous vehicle to traverse the environment based at least in part on the target trajectory.

RELATED APPLICATION

This application is a continuation-in-part of U.S. application Ser. No.16/179,679, filed Nov. 2, 2018, the entirety of which is incorporated byreference herein.

BACKGROUND

Various methods, apparatuses, and systems are utilized by autonomousvehicles to guide such autonomous vehicles through environmentsincluding various static and dynamic objects. For instance, autonomousvehicles utilize route planning methods, apparatuses, and systems toguide autonomous vehicles through congested areas with other movingvehicles (autonomous or otherwise), moving people, stationary buildings,etc. In some examples, generating a route may be computationallyintensive and/or may not provide a safe or comfortable route forpassengers.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical components or features.

FIG. 1 is a schematic diagram illustrating an example implementation ofa system for generating a trajectory, generating a drivable area, andevaluating costs, in accordance with embodiments of the disclosure.

FIG. 2 is an illustration of a reference trajectory comprising regionsof differing point densities, in accordance with embodiments of thedisclosure.

FIG. 3 is an illustration of determining weight(s) for a cost associatedwith generating a target trajectory based on a reference trajectory whennavigating around an obstacle, in accordance with embodiments of thedisclosure.

FIG. 4 is an illustration of determining weight(s) for a cost associatedwith generating a target trajectory based on a reference trajectory whenchanging lanes, in accordance with embodiments of the disclosure.

FIG. 5A is an illustration of a dilated region, a collision region, anda safety region associated with a drivable area, in accordance withembodiments of the disclosure.

FIG. 5B is an illustration of regions associated with a drivable areabased at least in part on a classification of an object in anenvironment and/or on a velocity of a vehicle in the environment, inaccordance with embodiments of the disclosure.

FIG. 6A is an illustration of updating a region based on identifying anobject such as a bicyclist proximate to the vehicle, in accordance withembodiments of the disclosure.

FIG. 6B is an illustration of determining a lane bias region and atarget trajectory based on the lane bias region, in accordance withembodiments of the disclosure.

FIG. 7 depicts a block diagram of an example system for implementing thetechniques described herein.

FIG. 8 depicts an example process for determining a point density forpoints associated with a reference trajectory and evaluating costs atthe points to generate a target trajectory, in accordance withembodiments of the disclosure.

FIG. 9 depicts an example process for determining weights for pointsassociated with a reference trajectory and evaluating, based on theweights, costs at the points to determine a target trajectory, inaccordance with embodiments of the disclosure.

FIG. 10 is an illustration of determining a cost at a location that isassociated with a portion of a trajectory, in accordance withembodiments of the disclosure.

FIGS. 11A-C are additional illustrations of determining one or morecosts associated and actions with one or more portions of a trajectory,in accordance with embodiments of the disclosure.

FIG. 12 depicts a block diagram of another example system forimplementing the techniques described herein.

FIG. 13 depicts an example process for determining a cost of a locationthat is associated with a portion of a trajectory and evaluating thecost to determine an action, in accordance with embodiments of thedisclosure.

DETAILED DESCRIPTION

As discussed above, generating a route for an autonomous vehicle throughan environment may be computationally intensive and/or may not provide asafe or comfortable route for passengers. This application describestechniques for reducing a computational burden of planning a routethrough an environment, may improve an accuracy and precision oftrajectory generation, and/or may improve safety and comfort of routesfor passengers. For instance, the techniques described herein may reducethe computational burden by adaptively scaling a density of trajectorypoints (e.g., points along the trajectory used for determiningassociated costs and controls) based on the level of activity (e.g.,cost(s) associated with curvature(s) and/or object(s)) along the route.By using a lower density of trajectory points for portions of the routethat have lower activity (e.g., lower cost(s) associated withcurvature(s) and/or fewer nearby object(s)), the computational intensityof trajectory generation can be reduced. Such costs as referred toherein may be, as non-limiting examples, proportional with respect tothe curvature or distance, an L1, L2, quadratic, Huber, polynomial,exponential, function or the like, including any combination thereof. Asanother example, the techniques described herein may increase accuracyand/or precision of trajectory generation by using a relatively higherdensity of trajectory points for portions of the route that have higheractivity (e.g., higher cost(s) associated with curvature(s) and/or morenearby object(s)). By way of another example, cost functionsquantitatively balance goals of comfort, vehicle dynamics, safety, andthe like. Techniques discussed herein include adaptively scaling weightsassociated with one or more costs to enhance safety and/or comfort whendetermining the contours of a trajectory through an environment.Further, regions establishing buffers around objects in an environmentcan be increased or decreased in size depending on a classification ofan object (e.g., pedestrians, vehicles, etc.) and/or depending on avelocity of the autonomous vehicle in the environment.

This disclosure is directed to techniques for generating trajectoriesand drivable areas for navigating a vehicle in an environment. Forexample, an autonomous vehicle can receive a reference trajectoryrepresenting an initial path or trajectory for the autonomous vehicle tofollow in an environment. A point density of points on the referencetrajectory can be based at least in part on a cost associated with acurvature of the reference trajectory and/or on a cost associated with adistance between the reference trajectory and an obstacle in theenvironment. A weight associated with various costs can be based ontriggers such as jumps or discontinuities in a reference trajectory orobstacle costs meeting or exceeding a threshold. Further, a drivablearea can represent a region in the environment where the autonomousvehicle can travel. Boundaries of the drivable region can includeinformation about object(s) in the environment and probabilisticdistances between the boundaries and the object(s). In some examples,regions associated with the drivable area can be based on aclassification of objects in the environment and/or on a velocity of theautonomous vehicle in the environment. A target trajectory can begenerated with respect to the reference trajectory based at least inpart on evaluating one or more costs at points on the referencetrajectory relative to the drivable area, whereby a density of points onthe reference trajectory and weights associated with cost(s) can bedetermined as discussed herein.

In some examples, a planning system of an autonomous vehicle can includeone or more layers for generating and optimizing one or moretrajectories for the autonomous vehicle to traverse an environment. Forexample, a first layer of the planning system can receive or determine alane reference (also referred to as a reference trajectory), which maycorrespond to or be associated with a center of a road segment. Costsassociated with points on the lane reference can be evaluated andoptimized to generate a first target trajectory. For example, a state ofthe vehicle can be evaluated along each point on the lane reference (orreference trajectory) to evaluate changing states of the vehicle overtime (e.g., sometimes referred to as a “rollout”). In some examples, thefirst target trajectory can be provided to a second layer of theplanning system, whereby the first target trajectory is used as areference trajectory. Costs associated with points on the referencetrajectory can be evaluated and optimized to generate a second targettrajectory. In some instances, the second target trajectory can beoptimized further or can be used to control the autonomous vehicle. Insome examples, the first layer can optimize the reference trajectorywith respect to a distance between points and/or the second layer canoptimize the reference trajectory with respect to a time between points,although other combinations are contemplated here.

In some examples, a reference trajectory representing an initial path ortrajectory for an autonomous vehicle to follow can be generated orreceived by a computing device of the autonomous vehicle. In someexamples, the reference trajectory can correspond to a centerline of aroad segment, although the reference trajectory can represent any pathin an environment. In some examples, points on the reference trajectorycan be established, selected, sampled, or otherwise determined based atleast in part on a cost associated with a curvature value associatedwith the reference trajectory and/or based at least in part on a costassociated with a distance between a point associated with a region ofthe reference trajectory and one or more obstacles in the environment.For example, a region associated with a relatively high curvature valuemay represent a maneuver around an obstacle in the environment or mayrepresent a maneuver such as turning a corner, parking, and the like. Byway of another example, a region of the reference trajectory can berelatively close to an obstacle in the environment. Accordingly, in thecase of a high cost associated with a curvature and/or of a high costassociated with a distance to an obstacle, the region can be associatedwith a relatively higher point density of points on the referencetrajectory. In some cases, one or more costs can be evaluated at thepoints on the reference trajectory to generate a target trajectory,whereby the autonomous vehicle can be controlled to follow the targettrajectory. Accordingly, dynamically varying the density of points onthe reference trajectory based on a cost associated with a curvature ofthe reference trajectory and/or based on a cost associated with adistance between the point on the reference trajectory and a pointassociated with an obstacle can affect an amount of downstreamprocessing in connection with generating a target trajectory.

In some examples, a target trajectory can be generated with respect tothe reference trajectory by evaluating one or more costs associated withpoints on the reference trajectory. In general, the one or more costsmay include, but is not limited to a reference cost, an obstacle cost, alateral cost, a longitudinal cost, and the like. Additional details ofthe one or more costs are provided below. Further, a cost can beassociated with a weight that can increase or decrease a cost associatedwith a trajectory or a cost associated with a point of the trajectory.In some examples, the operations discussed herein can include decreasinga weight associated with a cost based on triggers such asdiscontinuities in a reference trajectory or one or more costs meetingor exceeding a threshold. By way of example, and without limitation, aweight associated with a reference cost (discussed below) can bedecreased when the vehicle is navigating around a double-parked vehicleor while changing lanes. In some examples, a reference cost can comprisea cost associated with a difference between a point (also referred to asa reference point) on the reference trajectory and a corresponding point(also referred to as a point or a target point) on the targettrajectory, whereby the difference represents one or more difference ina yaw, lateral offset, velocity, acceleration, curvature, curvaturerate, and the like. In some examples, decreasing a weight associatedwith a reference cost can reduce a penalty associated with the targettrajectory being located a distance away from the reference trajectory,which can provide smoother transitions leading towards safer and/or morecomfortable vehicle operations. For example, decreasing the referencecosts associated with a portion of the reference trajectory can lead toincreasing a buffer between the vehicle and an obstacle in theenvironment.

In other examples, such as while changing lanes, selecting a weight fora reference cost can be combined with other checks to provide safe andcomfortable vehicle dynamics. For example, during a lane change action atarget trajectory can be checked to maintain a lateral displacement lessthan a threshold distance given a distance traveled forward by thevehicle and/or given a period of travel time. By way of example, andwithout limitation, a vehicle may be constrained to traveling 1 meter ina lateral direction for every 2 meters the vehicle travels in a forwarddirection, thereby reducing a sideways force during the lane change. Byway of another example, and without limitation, the vehicle may furtherbe constrained to traveling 1 meter in a lateral direction for a slidingwindow of time, such as two seconds. Of course, other distances andtimes are contemplated herein.

In some examples, an obstacle cost can comprise a cost associated with adistance between a point on the reference trajectory or the targettrajectory and a point associated with an obstacle in the environment.By way of example, the point associated with the obstacle can correspondto a point on a boundary of a drivable area or can correspond to a pointassociated with the obstacle in the environment. In some examples, anobstacle in the environment can include, but is not limited to a staticobject (e.g., building, curb, sidewalk, lane marking, sign post, trafficlight, tree, etc.) or a dynamic object (e.g., a vehicle, bicyclist,pedestrian, animal, etc.). In some examples, a dynamic object can alsobe referred to as an agent. In some examples, a static object or adynamic object can be referred to generally as an object or an obstacle.

In some examples, a lateral cost can refer to a cost associated withsteering inputs to the vehicle, such as maximum steering inputs relativeto a velocity of the vehicle.

In some examples, a longitudinal cost can refer to a cost associatedwith a velocity and/or acceleration of the vehicle (e.g., maximumbraking and/or acceleration).

As introduced above, the vehicle can determine a drivable area thatrepresents a region in the environment where the vehicle can travel. Insome examples, a computing device of an autonomous vehicle can receivesensor data captured by one or more sensors of the autonomous vehicleand can determine one or more objects in the environment and/orattributes of the one or more objects in the environment. In someexamples, the autonomous vehicle can utilize the object(s) and/or theattributes of the object(s) to determine which object(s) should beincluded in determining extents of the drivable area. Accordingly, theautonomous vehicle can plan a trajectory (e.g., a reference trajectoryand/or the target trajectory) within the extents of the drivable area.

In some examples, the drivable area can comprise a dilated region, acollision region, and/or a safety region. For example, the dilatedregion can be statically or dynamically generated with respect to a laneboundary to represent the largest extent of the drivable area, and cancomprise information about object(s) in the environment andprobabilistic distances between the boundaries and the object(s) and thereference and/or target trajectories. For example, the dilated regioncan represent a buffer associated with a boundary based at least in parton a distance (e.g., half of a width of a vehicle) plus some distancebased on an uncertainty of sensor noise, which may be based at least inpart on an object classification. Further, and in some examples, thecollision region can represent a smaller drivable area than the dilatedregion (e.g., representing a greater distance between an obstacle and aboundary of the collision region) representing a region for theautonomous vehicle to avoid to further reduce a likelihood that theautonomous vehicle will collide with an object in the environment. Insome examples, a cost associated with entering the collision region canbe relatively high (relative to the safety region). In some examples,the safety region can represent a region smaller than the collisionregion and the dilated region to provide a buffer between the autonomousvehicle and the object in the environment. In some examples, a costassociated with entering the safety region can be lower than a costassociated with the collision region. In some examples, the collisionregion and/or the safety region can also be associated with informationabout object(s) in the environment and probabilistic distances betweenthe boundaries and the object(s). In some examples, the autonomousvehicle can evaluate costs based at least in part on distance(s) betweenpoints on the reference trajectory and/or the target trajectory and oneor more points associated with the regions, as discussed herein. In someexamples, the cost(s) associated with the region(s) may vary. Forexample, a cost and/or weight associated with the safety region may berelatively less than a cost and/or weight associated with the collisionregion.

In some examples, a size of the region(s) can be based at least in parton a classification type of objects that the regions represent or areotherwise associated with in the environment. For example, a safetyregion associated with a pedestrian may be associated with a firstdistance or size and a safety region associated with a vehicle may beassociated with a second distance or size that is different than thefirst distance or size. By way of example, and without limitation, asafety region associated with the pedestrian can be larger than a safetyregion associated with a vehicle, which may result in the autonomousvehicle giving a larger buffer to the pedestrian than to the vehicle,assuming other factors are the same.

In some examples, a size of the region(s) can be based at least in parton a velocity of the autonomous vehicle. For example, a safety regionassociated with a pedestrian may be associated with a first distance orsize while the autonomous vehicle is traveling at a first velocity (oris predicted to travel at a reference point corresponding to the region)and may be associated with a second distance or size while theautonomous vehicle is traveling at a second velocity (or is predicted totravel at a reference point corresponding to the region) that isdifferent than the first velocity. By way of example, and withoutlimitation, a size of the safety region can be increased as the velocityof the autonomous vehicle increases, which may result in the autonomousvehicle giving a larger buffer to the pedestrian at a higher velocity,assuming other factors are the same.

The techniques discussed herein can improve a functioning of a computingdevice in a number of additional ways. In some cases, varying a densityof points associated with the reference trajectory can reduce an amountof processing by increasing a density of points in regions of highinterest or in regions with high activity and by decreasing a density ofpoints in regions of low interest or low activity. Further, becauseadditional processing steps, such as evaluating costs, are based on thepoints on the reference trajectory, reducing a number of points toconsider can represent a large reduction in overall processing withoutsacrificing accuracy or precision. In some examples, selecting weightsassociated with a cost can result in safer trajectories (e.g., byincreasing a distance between an object and a vehicle in theenvironment) and/or can result in more comfortable trajectories (e.g.,by reducing lateral acceleration during a lane change action). In someexamples, varying a size of regions associated with a drivable area canimprove safety by generating and/or modifying the regions based on aclassification type and/or a velocity of the autonomous vehicle.Further, the techniques discussed herein can be used alone or incombination to improve safety and/or comfort in a variety of systemsthat generate trajectories. These and other improvements to thefunctioning of the computer are discussed herein.

The techniques described herein can be implemented in a number of ways.Example implementations are provided below with reference to thefollowing figures. Although discussed in the context of an autonomousvehicle, the methods, apparatuses, and systems described herein can beapplied to a variety of systems (e.g., a sensor system or a roboticplatform), and are not limited to autonomous vehicles. In anotherexample, the techniques can be utilized in an aviation or nauticalcontext. Additionally, the techniques described herein can be used withreal data (e.g., captured using sensor(s)), simulated data (e.g.,generated by a simulator), or any combination of the two.

FIG. 1 is a schematic diagram illustrating an example implementation ofa system for generating a trajectory, generating a drivable area, andevaluating costs, in accordance with embodiments of the disclosure. Morespecifically, FIG. 1 illustrates an example environment 100 in which avehicle 102 is positioned. In the illustrated example, the vehicle 102is driving in the environment 100, although in other examples thevehicle 102 may be stationary and/or parked in the environment 100. Oneor more objects, or agents, also are in the environment 100. Forinstance, FIG. 1 illustrates additional vehicles 104 a and 104 b andpedestrians 106 a and 106 b in the environment 100. Of course, anynumber and/or type of objects can additionally or alternatively bepresent in the environment 100.

For the purpose of illustration, the vehicle 102 can be an autonomousvehicle configured to operate according to a Level 5 classificationissued by the U.S. National Highway Traffic Safety Administration, whichdescribes a vehicle capable of performing all safety-critical functionsfor the entire trip, with the driver (or occupant) not being expected tocontrol the vehicle at any time. In such an example, since the vehicle102 can be configured to control all functions from start to stop,including all parking functions, it can be unoccupied. This is merely anexample, and the systems and methods described herein can beincorporated into any ground-borne, airborne, or waterborne vehicle,including those ranging from vehicles that need to be manuallycontrolled by a driver at all times, to those that are partially orfully autonomously controlled. Additional details associated with thevehicle 102 are described below.

In the example of FIG. 1, the vehicle 102 can be associated with one ormore sensor systems 108. The sensor system(s) 108 can generate sensordata 110, which can be utilized by vehicle computing device(s) 112associated with the vehicle 102 to recognize the one or more objects,e.g., the vehicles 104 and the pedestrians 106. The sensor system(s) 108can include, but is/are not limited to, light detection and ranging(LIDAR) sensors, radio detection and ranging (RADAR) sensors, Time ofFlight sensors, ultrasonic transducers, sound navigation and ranging(SONAR) sensors, location sensors (e.g., global positioning system(GPS), compass, etc.), inertial sensors (e.g., inertial measurementunits, accelerometers, magnetometers, gyroscopes, etc.), cameras (e.g.,RGB, IR, intensity, depth, etc.), wheel encoders, microphones,environment sensors (e.g., temperature sensors, humidity sensors, lightsensors, pressure sensors, etc.), etc.

In at least one example, the vehicle computing device(s) 112 can includea perception system, which can perform object detection, segmentation,and/or classification based at least in part on the sensor data 110received from the sensor system(s) 108. For instance, the perceptionsystem can detect the vehicles 104 a and 104 b and/or the pedestrians106 a and 106 b in the environment 100 based on the sensor data 110generated by the sensor system(s) 108. Additionally, the perceptionsystem can determine an extent of the vehicles 104 a and 104 b and/orthe pedestrians 106 a and 106 b (e.g., height, weight, length, etc.), apose of the vehicles 104 a and 104 b and/or the pedestrians 106 a and106 b (e.g., x-coordinate, y-coordinate, z-coordinate, pitch, roll,yaw), etc. The sensor system(s) 108 can continuously generate the sensordata 110 (e.g., in near-real time), which can be utilized by theperception system (and other systems of the vehicle computing device(s)112).

The vehicle computing device(s) 112 can also include a drivable areacomponent 114, a trajectory generation component 116, and a cost(s)component 118. In some examples, although discussed separately, thetrajectory generation component 116 and the cost(s) component 118(and/or other components discussed herein) can be integrated into asingle component or algorithm. In general, the drivable area component114 can include functionality to generate and/or determine a drivablearea 120, in accordance with the embodiments discussed herein. Ingeneral, the trajectory generation component 116 can includefunctionality to generate a reference trajectory 122 and/or a targettrajectory 124 within the drivable area 120, in accordance with theembodiments discussed herein. In general, the cost(s) component caninclude functionality to evaluate one or more costs to generate thetarget trajectory 124 with respect to the reference trajectory 122.

In some examples, the drivable area 120 may be a virtual area in theenvironment 100 in which the vehicle 102 can travel safely through theenvironment 100, for example, relative to objects including the vehicles104 a, 104 b and the pedestrians 106 a, 106 b.

As illustrated in FIG. 1 and detailed further herein, the drivable area120 may be defined by virtual boundaries 126 and 128. In the exampledrivable area 120 illustrated in FIG. 1, the drivable area 120 may havea variable width 130, for example, in the width or lateral direction ofthe vehicle 102. FIG. 1 illustrates a first width 130 a and a secondwidth 130 b, which collectively, and along with other widths at otherpositions in the drivable area 120, may be referred to as the width 130.As described herein, attributes of the virtual boundaries 126 and 128,including the width 130, may be determined based at least in part on thesensor data 110 and/or determinations made by the perception system. Forexample, in some implementations, the virtual boundaries 126 and 128 maybe determined based on information about the objects in the environment100, which may include information about semantic classifications and/orprobabilistic models. In at least some examples, the virtual boundaries126 and 128 may be encoded with information (such as lateral distance tothe nearest object, semantic classification of the nearest object,related probabilities of such distance and classification, etc.). Insome examples, the drivable area 120 can be generated in accordance withthe techniques discussed in U.S. patent application Ser. No. 15/982,694,filed May 17, 2018, which is hereby incorporated by reference, in itsentirety.

As also illustrated in FIG. 1, upon determining the drivable area 120,the vehicle computing device(s) 112 also may determine a referencetrajectory 122 along which the vehicle 102 may travel through theenvironment 100. The planner system can determine routes and/ortrajectories to use to control the vehicle 102 based at least in part onthe sensor data 110 received from the sensor system(s) 108 and/or anydeterminations made by the perception system. For instance, the planningsystem can evaluate one or more costs to generate the target trajectory124 with respect to the reference trajectory 122 to navigate safelythrough the environment relative to the vehicle 104 a, 104 b and thepedestrians 106 a, 106 b.

More specifically, FIG. 1 illustrates a scenario in which the vehicle102 is travelling through the environment 100, generally in thedirection of arrow 132. The vehicle 102 is travelling on a road 134having a first lane 136 and a second lane 138. The vehicle 102 is in thefirst lane 136, behind the vehicle 104 a, and, in the example, istravelling relatively faster than the vehicle 104 a. For example, thevehicle 104 a may be slowing down to turn left, to parallel park on theleft side of the road 134, to drop off a passenger or delivery on theleft shoulder of the road, or the like. In examples of this disclosure,the vehicle 102, for example, by executing the drivable area component114, may determine the drivable area 120 as a precursor to determiningwhether the vehicle 102 can safely navigate around the slowing vehicle104 a. More specifically, the vehicle computing device(s) 112, using thesensor data 110, may determine information about objects in theenvironment 100, which may include the vehicle 104 a, the vehicle 104 b,the pedestrian 106 a, the pedestrian 106 b, and/or additional objects.For example, the vehicle computing device(s) 112, using the drivablearea component 114, may fuse or combine information about each of theobjects to configure the drivable area 120. Such a drivable area 120may, in turn, be used to determine the reference trajectory 122 and/orthe target trajectory 124 (e.g., by providing constraints and/orboundaries) along which the vehicle 102 may travel. In some examples,the reference trajectory 122 can be generated prior to or substantiallysimultaneously (within technical tolerances) as the drivable area 120.

With further reference to FIG. 1, the drivable area 120 may have varyingwidths, for example, the first width 130 a and a second width 130 b, andthose widths are determined at least in part based on information aboutthe objects in the environment 100. For example, at a positionimmediately in front of the vehicle 102 in the direction illustrated byarrow 132, the first width 130 a generally spans the entire width of theroad 134. At edges of the first lane 136 and the second lane 138, aslight offset relative to the shoulder may be provided, although such anoffset may be excluded in other embodiments. Further along the road 134in the direction of the arrow 132, the drivable area 120 begins tonarrow as the virtual boundary 128 gradually moves away from the leftshoulder until the drivable area 120 is completely confined in the rightlane (second lane 138). This narrowing of the drivable area 120 is aresult of the presence and/or the relative deceleration of the vehicle104 a. Moreover, immediately adjacent the vehicle 104 a, the drivablearea 120 may further narrow, for example, to provide a minimum lateraloffset relative to the vehicle 104 a. Similarly, on the right side ofthe drivable area 120, offsets may be provided proximate the pedestrian106 a and the vehicle 104 b, which is parked on the right shoulder ofthe road 134. In some examples, such a drivable area 120 may also bedetermined based on a predicted trajectory of objects, such as that ofthe pedestrian 106 a. As a non-limiting example depicted in FIG. 1, thedrivable area 120 indents slightly near the pedestrian 106 a inanticipation of the pedestrian 106 a continuing walking in the directionof travel (e.g., direction of the arrow 132). The length of such anindent may be based on, for example, the velocities of the vehicle 102and the pedestrian 106 a.

As described further herein, the width 130 of the drivable area 120varies as a result of information about objects in the environment 100.Thus, for example, offsets are provided proximate the vehicle 104 a, thevehicle 104 b, and the pedestrian 106 a. These offsets may serve toprovide a safety margin or minimum distance of the vehicle 102 from theobjects, as the vehicle 102 travels in the drivable area 120. In someimplementations, the offsets and/or the minimum distances may differamong and between objects in the environment 100. For example, semanticinformation about the objects may be used to determine appropriateoffsets. Thus, in FIG. 1, pedestrians may be associated with a firstclassification and vehicles may be associated with a secondclassification. The first classification may correspond to a minimumoffset larger than a minimum offset to which the second classificationcorresponds. Moreover, vehicles within the second classification may befurther classified as static, as in the case of the vehicle 104 b ordynamic, as in the case of the vehicle 104 a. An offset relative to themoving vehicle 104 a may be greater than the offset associated with theparked vehicle 104 b, for example. In further examples, probabilisticinformation about the objects may also or alternatively be used todetermine the offsets. For example, there may be some uncertaintyassociated with the actions of the moving pedestrian 106 a and/or themoving vehicle 104 a, for example, because the pedestrian 106 a or thedriver of the vehicle 104 a may change course. Probabilistic informationmay also be used to account for sensor inaccuracies, for example,associated with the sensor system(s) 108.

The drivable area 120 may allow for more efficient navigation around thevehicle 104 a when compared with conventional navigation techniques. Inconventional solutions, for example, the vehicle 102 may slow down as itapproaches the vehicle 104 a, for example, to maintain a minimumdistance between the vehicle 102 and the vehicle 104 a, and only uponstopping or slowing to a threshold speed may the vehicle 102 begin toseek out an alternative route around the vehicle 104 a. Alternatively,the vehicle 102 may idle in the first lane 136 until the vehicle 104 aturns, parks, moves, accelerates, or the like. However, inimplementations of this disclosure, the drivable area 120 considersmultiple objects as well as information about the objects to provide amore robust understanding of the environment 100. This understanding mayenhance decision making and/or allow for more effective and efficientcontrol of the vehicle 102 as it travels through the environment 100.With specific regard to FIG. 1, such enhanced decision-making may allowthe vehicle to traverse along the target trajectory 124, without theneed to slow down behind the vehicle 104 a and/or await additionaldecision making. Further, the drivable area 120 provides boundaries fora wide range of possible trajectories which may be employed to safelynavigate a portion of a path. By providing such a wide range of safetrajectories, the vehicle 102 is able to quickly overcome any hazards,while incorporating information about all objects in a scene.

FIG. 1 illustrates a single example of using a drivable area to navigatein an environment. Myriad other examples also are contemplated. Forexample, the drivable area component 114 may be used in any environmentto better navigate with respect to objects.

FIG. 2 is an illustration of a reference trajectory comprising regionsof differing point densities, in accordance with embodiments of thedisclosure. More specifically, FIG. 2 illustrates an example environment200 in which the vehicle 102 is positioned. FIG. 2 further illustratesan obstacle 202 in the environment 200, whereby a reference trajectory204 represents an initial path for the vehicle 102 to navigate aroundthe obstacle 202 in the environment 200. In some examples, the obstacle202 corresponds to the vehicle 104 a of FIG. 1, although the obstacle202 may represent any static object or dynamic object in the environment200. Further, the reference trajectory 204 may correspond to thereference trajectory 122 of FIG. 1, although the reference trajectory204 may represent any path or trajectory in the environment 200.

In some examples, the trajectory generation component 116 (or anothercomponent of the vehicle computing device(s) 112) can determine adensity of points associated with various regions of the referencetrajectory 204. By way of example and without limitation, the vehiclecomputing device(s) 112 can identify region(s) of the referencetrajectory 204 corresponding to regions of interest or corresponding toregions of high activity. In some examples, the region(s) can beidentified based at least in part on a cost associated with a curvaturevalue associated with the reference trajectory 204 and/or based at leastin part on a cost associated with a distance between a point on thereference trajectory 204 and a distance to a point associated with anobstacle in the environment 200.

As illustrated, the reference trajectory 204 includes portions 206, 208,210, and 226, although the reference trajectory 204 can include anynumber of portions.

The portion 206 can be associated with a point density 212, the portion208 can be associated with a point density 214, the portion 210 can beassociated with a point density 216, and the portion 226 can beassociated with a point density 228.

In some examples, the point density 214 can be higher than the pointdensity 212 and/or 216 because a cost associated with a curvature valueassociated with the portion 208 is higher than a curvature valueassociated with the portions 206 and/or 210. In some examples, the pointdensity 228 can be higher than the point density 212 and/or 217 becausea cost associated with a distance between the portion 226 and theobstacle 202 is smaller than a distance between the portions 206, 210and the obstacle 202. That is, the point density 214 associated with theportion 208 can be based at least in part on a cost associated with acurvature value associated with the portion 208 and the point density228 associated with the portion 226 can be based at least in part on acost associated with a distance between the portion 226 and the obstacle202.

In some examples, the point density 214 can correspond to a density ofpoints in space or time. For example, as discussed above, a first layerof a planning system may optimize a trajectory based on a distance(which may be a Euclidian distance, length along an inertial reference,etc.) between points while a second layer of a planning system mayoptimize the trajectory based on a time period between points, or viceversa. In some examples, the difference 218 between adjacent referencepoints 220 and 222 can represent a distance in space (e.g., 5 cm, 10 cm,50 cm, 1 m, and the like) or a distance in time (e.g., 0.05 seconds, 0.1seconds, 0.2 seconds, 0.5 seconds, 1 second, and the like). In anexample where the point density 214 is higher than the point density212, the difference 218 between the adjacent reference points 220 and222 can be smaller than a difference 224 between adjacent referencepoints associated with the portion 206.

As can be understood in the context of this disclosure, one or morecosts can be evaluated at discrete points on the reference trajectory204 to generate a target trajectory that the vehicle 102 can follow totraverse through the environment 200.

FIG. 3 is an illustration of determining weight(s) associated with acost associated with generating a target trajectory based on a referencetrajectory when navigating around an obstacle, in accordance withembodiments of the disclosure. More specifically, FIG. 3 illustrates anexample environment 300 in which the vehicle 102 is positioned. FIG. 3further illustrates an obstacle 302 in the environment 300, whereby areference trajectory 304 represents an initial path for the vehicle 102to navigate around the obstacle 302 in the environment 200. In someexamples, the reference trajectory 304 can correspond to a maneuver forthe vehicle 102 to navigate around a double-parked vehicle in theenvironment 300. In some examples, the obstacle 302 may represent anystatic object or dynamic object in the environment 300. Further, thereference trajectory 304 may represent any path in the environment 300.

In general, the vehicle computing device(s) 112 can evaluate one or morecosts at reference points 306 and 308 to generate a target trajectory310 having points 312 and 314 corresponding to the reference points 306and 308, respectively. A dotted line between points on the referencetrajectory 304 and the target trajectory 310 indicates correspondingpoints. In some cases, each point on the reference trajectory 304 can beassociated with a corresponding point on the target trajectory 310,although in some cases the target trajectory 310 can be associated withpoints that do not necessarily correspond to point(s) on the referencetrajectory. In some cases, a point (e.g., the points 312, 314, and 326)on the target trajectory 310 can be orthogonal to a correspondingreference point (e.g., the reference points 306, 308, and 324,respectively) on the reference trajectory 304, based at least in part ona curvature value at the reference trajectory 304.

In general, the one or more costs may include, but is not limited to areference cost, an obstacle cost, a lateral cost, a longitudinal cost,and the like. A cost can be associated with a weight (e.g., weights 316and 318) that can increase or decrease a cost associated with atrajectory or a cost associated with a point of the trajectory. In someexamples, the vehicle computing device(s) 112 can perform operations todecrease a weight associated with a cost based on actions or maneuversto be performed by the vehicle.

A start trigger 320 can represent a condition causing weights associatedwith cost(s) to be varied, as discussed herein. In some examples, atrigger can correspond to obstacle costs meeting or exceeding athreshold (e.g., discussed in context of FIG. 3) and/or jumps ordiscontinuities in a reference trajectory (e.g., discussed in thecontext of FIG. 4). For example, during an operation to evaluate initialcosts associate with reference points on the reference trajectory 304,an obstacle cost associated with a reference point 324 may meet orexceed a threshold cost by virtue of the reference point 324 beinglocated within the obstacle 302. Accordingly, the start trigger can beinitiated along the reference trajectory 304 based at least in part on adistance or time in relation to the reference point 324. An end trigger322 can represent a period of time or distance associated with thecost(s) (e.g., an obstacle cost) associated with the referencetrajectory 304 being below a threshold value. Based at least in part onthe start trigger 320 and the end trigger 322, a weight associated withthe points of the reference trajectory 304 and/or the target trajectory310 can be adjusted to increase or decrease the weights. For example, aweight associated with the reference point 306 can correspond to theweight 316 and a weight associated with the reference point 308 cancorrespond to the weight 318. Accordingly, evaluating costs associatedwith the points 306 and 308 can be based at least in part on the weights316 and 318, respectively. By reducing the weight associated with thereference cost when passing the obstacle 302, the vehicle is better ableto explore trajectories which may be further from the referencetrajectory 304 to overcome the obstacle 302. By way of example, a point326 corresponding to the reference point 324 is allowed to be locatedfurther from the reference point 324 to safely overcome the obstacle302.

In some examples, the weights associated with the reference points 306,308, and 324 can be used to determine the points 312, 314, and 326respectively, and accordingly, can determine the contours of the targettrajectory 310. By way of example, and without limitation, a weightassociated with a reference cost can be decreased when the vehicle isnavigating around the obstacle 302. In some examples, a reference costcan comprise a cost associated with a difference between a point (e.g.,the reference point 308) on the reference trajectory 304 and acorresponding point (e.g., the point 314) on the target trajectory 310.In some example, the difference can represent one or more difference ina yaw, lateral offset, velocity, acceleration, curvature, curvaturerate, and the like. In some examples, decreasing a weight associatedwith a reference cost can reduce a penalty associated with the targettrajectory 310 being located a distance away from the referencetrajectory 304, which can provide smoother transitions leading towardssafer and/or more comfortable vehicle operations. For example,decreasing the reference costs associated with a portion of thereference trajectory 304 associated with the start trigger 320 and theend trigger 322 can lead to increasing a buffer between the vehicle 102and the obstacle 302 in the environment 300 and/or otherwise allowingthe vehicle 102 to explore trajectories not previously available.

FIG. 4 is an illustration of determining weight(s) associated with acost associated with generating a target trajectory based on a referencetrajectory when changing lanes, in accordance with embodiments of thedisclosure. More specifically, FIG. 4 illustrates an example environment400 in which the vehicle 102 is positioned. FIG. 4 further illustratesan obstacle 402 in the environment 400, whereby a reference trajectory404 (comprising portions 404A and 404B) represents an initial path ortrajectory for the vehicle 102 to change lanes to navigate around theobstacle 402 in the environment 400. In some examples, the obstacle 402corresponds to the vehicle 104 a of FIG. 1, although the obstacle 402may represent any static object or dynamic object in the environment200. Further, the reference trajectory 404 may represent an alternativeimplementation compared to the reference trajectory 122 of FIG. 1 (e.g.,representing a lane change action from the first lane 136 to the secondlane 138). However, the reference trajectory 404 may represent any pathor trajectory in the environment 400.

In general, the vehicle computing device(s) 112 can evaluate one or morecosts at a reference point 406 to generate a target trajectory 408having a trajectory point 410 corresponding to the reference point 306.A dotted line between the reference point 406 and the trajectory point410 indicates a corresponding point. Although only one point on thereference trajectory 404 is shown in FIG. 4, it can be understood that aplurality of points on the reference trajectory 404 can correspond to aplurality of corresponding points on the target trajectory 408.

In general, the one or more costs may include, but is not limited to areference cost, an obstacle cost, a lateral cost, a longitudinal cost,and the like. A cost can be associated with a weight (e.g., weights 412and 414) that can increase or decrease a cost associated with atrajectory or a cost associated with a point of the trajectory. In someexamples, the vehicle computing device(s) 112 can perform operations todecrease a weight associated with a cost based on actions or maneuversto be performed by the vehicle.

A start trigger 416 can represent a jump or discontinuity of thereference trajectory 404, which can represent a lane change action tochange from the first lane 136 to the second lane 138 in the environment400. A curve 418 can represent the varying weight(s) of cost(s) over thereference trajectory 404B, whereby a shape of the curve 418 can beadjusted based on a lateral distance for the vehicle 102 to traversewith respect to a distance and/or time. Based at least in part on thestart trigger 416 and the curve 418, a weight associated with the pointsof the reference trajectory 404 and/or the target trajectory 408 can beadjusted to increase or decrease the weight. In some examples, the curve418 can represent a linear function, quadratic function, stepwisefunction, and the like, associated with the weight. For example, aweight associated with the reference point 406 can correspond to theweight 414. Accordingly, evaluating cost(s) associated with thereference point 406 can be based at least in part on the weight 414. Ofcourse, evaluating the costs can be performed for a plurality of pointsassociated with a plurality of weights, as discussed herein.

In some examples, a lane change action can be associated with otherconstraints to provide a smooth and comfortable behavior of the vehicle102. For example, a detail of the target trajectory 408 is illustratedas a detail 420 of a portion of the target trajectory 422. In an example424, the vehicle computing device(s) 112 can determine whether a lateraldisplacement 426 of the vehicle 102 is less than a threshold amountgiven a longitudinal displacement 428. By way of example and withoutlimitation, the lateral displacement 426 may be 1 meter (or N meters)and the longitudinal displacement 428 may be 5 meters (or M meters). Inan example 430, the vehicle computing device(s) 112 can determinewhether a lateral displacement 432 of the vehicle 102 is less than athreshold amount given a time period 434. By way of example and withoutlimitation, the lateral displacement 432 may be 1 meter (or N′ meters)and the longitudinal displacement 428 may be 2 seconds (or S seconds).In some examples, a shape of the curve 418 can be adjusted (e.g., tovary weights more or less rapidly) to ensure that the lateraldisplacement 426 and 432 of the vehicle 102 is below a threshold. Thus,in some examples, the vehicle computing device(s) 112 can vary weight(s)associated with cost(s) and can ensure that a lateral displacement ofthe vehicle 102 is less than a threshold amount given a distance totravel and given a time period to determine the target trajectory 408for the vehicle 102 to follow as the vehicle 102 traverses theenvironment 400.

FIG. 5A is an illustration of a dilated region, a collision region, anda safety region associated with a drivable area, in accordance withembodiments of the disclosure. In particular, FIG. 5 illustrates anexample environment 500 in which the vehicle 102 is positioned in a lane502 of a road 504. FIG. 5 further illustrates a pedestrian 106 atraversing the environment 500 and the vehicle 104 a parked on the sideof the road 504.

In some examples, the vehicle computing device(s) 112 can determine adrivable area 506 comprising a plurality of regions, as illustrated in adetail 508. In particular, the drivable area 506 comprises a dilatedregion 510, a collision region 512, and a safety region 514. Forexample, the dilated region 510 can represent the largest extent of thedrivable area 506 and can comprise information about object(s) (e.g.,pedestrian 106 a and vehicle 104 a) in the environment 500 andprobabilistic distances between the boundaries (e.g., defined by theregions 510, 512, and 514) and the object(s). As a non-limiting example,uncertainties from measurement and/or movement of the object(s) may beeffectively “encoded” into such boundaries. Further, and in someexamples, the collision region 512 can represent a drivable regionsmaller than the dilated region 510 representing a region for thevehicle 102 to avoid to further reduce a likelihood that the vehicle 102will collide with the pedestrian 106 a and the vehicle 104 a in theenvironment 500. In some examples, the safety region 514 can represent adrivable region smaller than the collision region 512 and the dilatedregion 510 to provide a buffer between the vehicle 102 and thepedestrian 106 a and the vehicle 104 a in the environment 500. In someexamples, the collision region 512 and/or the safety region 514 can alsobe associated with information about object(s) in the environment andprobabilistic distances between the boundaries and the object(s). Insome examples, the vehicle 102 can evaluate costs based at least in parton distance(s) between points on a reference trajectory 516 (and/or on atarget trajectory) and one or more points associated with the regions510, 512, and/or 516, as discussed herein. In some examples, the cost(s)associated with the region(s) 510, 512, and 514 may vary. For example, acost associated with the safety region 514 may be relatively less than acost associated with the collision region 512, and vice versa.

FIG. 5B is an illustration of regions associated with a drivable areabased at least in part on a classification of an object in anenvironment and/or on a velocity of a vehicle in the environment, inaccordance with embodiments of the disclosure. An example 518illustrates the detail 508 of FIG. 5A with the dilated region 510, thecollision region 512, and the safety region 514.

For example, the safety region 514 represents a safety region while thevehicle 102 is associated with a velocity V₁.

By way of another example, the safety region 514* represents a safetyregion while the vehicle 102 is associated with a velocity V₂. As thesafety regions 514 and 514* can be associated with information aboutobject(s) in the environment 500 (e.g., classification information) andprobabilistic distances between the boundaries and the object(s), a sizeof different portions of the safety regions 514 and 514* can vary basedon classification information of the different portions and a velocityof the vehicle 102. For example, a portion 520 can represent a portionof the safety regions 514 and 514* associated with the pedestrian 106 a,while the portion 522 can represent a portion of the safety regions 514and 514* associated with the vehicle 104 a. Portions 524, 526, and 528may not be associated with the objects (e.g., pedestrian 106 a orvehicle 104 a), or may be associated with the extents of the road 504.

In some examples, a size of the safety regions 514 and 514* can be basedat least in part on classification information of the associated objectand/or on a velocity of the vehicle 102 in the environment. For example,a distance 530 associated with the portion 520 of the safety region 514*can represent an increase in a size of the safety region 514 for theportion associated with the pedestrian 106 a while the vehicle 102 is ata velocity V₂. A distance 532 associated with the portion 522 of thesafety region 514* can represent an increase in a size of the safetyregion 514 for the portion associated with the vehicle 104 a while thevehicle 102 is at a velocity V₂. In some examples, the distance 530 canbe larger than the distance 532. In some examples, the velocity V₂ canbe higher than the velocity V₁. In some examples, varying a size of thesafety regions 514 and 514* can further include modifying an extent ofthe drivable area to generate a modified drivable area.

In some examples, the portions 524, 526, and/or 528 of the safety region514 that are not associated with the pedestrian 106 a or the vehicle 104a may or may not change based on a velocity of the vehicle 102.

FIG. 6A is an illustration of updating a region based on identifying anobject such as a bicyclist proximate to the vehicle, in accordance withembodiments of the disclosure. In particular, FIG. 6A illustrates anexample environment 600 in which the vehicle 102 is positioned in a lane602 of a road 604. FIG. 6A further illustrates a pedestrian 106 atraversing the environment 500, the vehicle 104 a parked on the side ofthe road 604, and a bicyclist 606 (e.g., in a bicycle lane) proximate tothe vehicle 102.

A detail 608 illustrates the vehicle 102, the bicyclist 606 proximate tothe vehicle 102, and a drivable area 610 comprising a plurality ofregions. For simplicity of discussion, a lane boundary 612 and a lanebias region 614 are illustrated in FIG. 6A, although it may beunderstood that additional regions may comprise a dilated region, acollision region, a safety region, and the like. In some examples, thevehicle computing device(s) 112 can determine the lane bias region 614based at least in part on the bicyclist 606 (and/or a bicycle lane,parking lane, parked car, and the like) being proximate to the vehicle102 in the environment 600.

For example, the vehicle computing device(s) 112 can determine apredicted trajectory for the bicyclist 606 to determine that thebicyclist 606 may pass the vehicle 102, and accordingly, can expand asize of the lane boundary 612 to generate the lane bias region 614.Accordingly, for a given reference trajectory 616, the vehicle computingdevice(s) 112 can generate a target trajectory 618 that biases alocation of the vehicle 102 away from the bicyclist 606 (and/or bicyclelane) to allow the bicyclist to pass the vehicle 102 with morecomfortable space. In some examples, a determination of whether togenerate the lane bias region 614 can be based at least in part on oneor more of a velocity of the bicyclist 606, a velocity of the bicyclist606 relative to a velocity of the vehicle 102, a velocity of the vehicle102 being below a threshold, a position of the bicyclist 606 in the lane602 of the road 604, a position of the vehicle 102 and/or informationfrom a map, and the like. In this manner, the vehicle computingdevice(s) 112 can evaluate cost(s) based on the lane bias region 614 togenerate the target trajectory 618 that provides additional space forthe bicyclist 606 to pass the vehicle 102.

FIG. 6B is an illustration of determining a lane bias region 614 and atarget trajectory 618 based on the lane bias region 614. As discussedabove, the vehicle 102 can determine a first drivable area 624associated with a first width which can be similar to the drivable area610 depicted in FIG. 6A. Additionally, the vehicle computing device(s)112 can determine a predicted trajectory for the bicyclist 606 and, asan example and as depicted in FIG. 6B, determine that the vehicle 102may pass the bicyclist 606. Although depicted as a bicyclist, thebicyclist 606 can be any object such as a pedestrian, another vehicle,and the like. Upon satisfaction of at least one of the conditionsdiscussed herein, the vehicle computing device(s) 112 can determine alane bias region 614 which can provide a region for the bicyclist 606 totraverse. To determine the lane bias region 614, the vehicle computingdevice(s) 112 can expand a size, or move a location, of the laneboundary 612 of the first drivable area 624 to virtual boundary 622.Based at least in part on the virtual boundary 622, a second drivablearea 626 associated with a second width can be determined. The lane biasregion 614 can be based on a difference between the first width and thesecond width which can represent the lane bias region 614.

As the vehicle 102 traverses the environment 620, the sensor system(s)108 can generate sensor data 110 and the vehicle computing device(s) 112can use the sensor data 110 to detect an object (e.g., bicyclist 606) inthe environment 620. In some examples, the vehicle computing device(s)112 can use the sensor data 110 to determine an object type (e.g., avehicle, a pedestrian, a bicyclist, an animal, a static object, adynamic object, and the like) associated with the object. For example,the vehicle computing devices(s) 112 can perform object detection,segmentation, and/or classification based at least in part on the sensordata 110 to determine the object type. In some examples, the vehiclecomputing device(s) 112 may comprise one or more machine learningalgorithms trained to detect, segment, and/or classify sensor data inassociation with an object.

In some instances, the vehicle computing device(s) 112 can use theobject type to determine a distance threshold (e.g., a first distancethreshold 628) indicating a distance between the vehicle 102 and theobject. The vehicle computing device(s) 112 can determine that at leasta portion of the object is within the distance threshold and based ondetermining that at least a portion of the object is within the distancethreshold, determine the lane bias region 614, which as discussed above,can include determining a second drivable area 626 that is associatedwith a width that is less than the width associated with the firstdrivable area 624.

In some instances, the distance can be the first distance threshold 628and can indicate an upper and/or lower longitudinal, lateral, and/orEuclidean distance between the vehicle 102 and object (e.g., thebicyclist 606). The first distance threshold 628 can be stored in memoryand/or can be determined by the vehicle computing device(s) 112 based atleast in part on a velocity of the vehicle 102 and/or a velocity of theobject. By way of example and without limitation, a longitudinaldistance threshold associated with a pedestrian object type can be 30meters (or any other suitable distance, e.g., 20 meters, 10 meters,etc.), indicative of the pedestrian being 30 meters ahead of the vehiclealong the road. The vehicle computing device(s) 112 can then detect anobject that is a pedestrian in the environment 620 and determine theobject type of the object which can be associated with the distancethreshold of 30 meters.

In some instances, an upper distance threshold and a lower distancethreshold can be used to determine the lane bias region 614. The upperdistance threshold and lower distance threshold may indicate a range ofdistances between the vehicle and the object for which the vehiclecomputing device(s) 112 may calculate a lane bias region 614. Forexample, if a lateral distance, or a predicted lateral distance(discussed in further detail below), between an object and the vehicle102 meets or exceeds the upper distance threshold, then the vehicle 102can refrain from determining the lane bias region 614. By meeting orexceeding the upper distance threshold, the vehicle computing device 112can determine that enough of a safety region between the vehicle 102 andthe object exists without the lane bias region 614. If the lateraldistance, or the predicted lateral distance, is below the upper distancethreshold, then the vehicle computing device 112 can determine the lanebias region 614 to, as discussed above, provide a region for the objectto traverse.

In some instances, a lower distance threshold can be used to determinethe lane bias region 614. For example, if a lateral distance, or apredicted lateral distance, between the object and the vehicle 102 isbelow the lower distance threshold, then the vehicle 102 can refrainfrom determining the lane bias region 614. In some such cases, beingbelow a lower distance threshold may be indicative of the fact thatthere is insufficient room to perform such a lane biasing maneuver. Byway of example and without limitation, a longitudinal distance betweenan object and the vehicle 102 can be below the first distance threshold628. In some instances, the object can be directly in front of thevehicle 102 where the lateral distance between the object and thevehicle 102 is minimal. However, because the object is in front of thevehicle 102, determining a lane bias region 614 is not necessary.Therefore, a lower distance threshold can be used to associate theobject with a lateral portion of the vehicle 102.

For example, the vehicle computing device(s) 112 can determine a laterallower distance threshold (e.g., second distance threshold 630) of 0.4meters (or any other suitable distance, e.g., 0.5 meters, 2 meters,etc.) that is associated with a bicyclist. As the vehicle 102 approachesthe bicyclist, or vice versa, so long as the lateral distance betweenthe vehicle 102 and the bicyclist is below the lower distance threshold,indicating that the bicyclist is substantially in front or behind thevehicle 102, the vehicle 102 can maintain a current trajectory. In suchcircumstances, it can be safer for the vehicle 102 to monitor thebicyclist and react if necessary as opposed to adjusting a position in alane (as may be done by determining a lane bias region 614 that can pushthe vehicle 102 toward other objects in the environment 620). If thelateral distance between the vehicle 102 and the bicyclist meets orexceeds the lower distance threshold, then the vehicle can determine thelane bias region 614 to provide a region for the bicyclist to traverse.

In some instances, the distance threshold can be a time threshold basedon an amount of time. For example, the vehicle computing device(s) 112can determine, based on a speed associated with the vehicle 102 and/or aspeed associated with the object, that the object is an amount of timefrom the vehicle 102. By way of example and without limitation, thevehicle can travel at a speed of 10 meters per second and detect thatthe object is traveling at a speed of 5 meters per second at a distanceof 20 meters ahead of the vehicle 102. The vehicle computing device(s)112 can determine a time threshold (which may be based on a distancebetween the vehicle and an object, a speed of the vehicle, a speed ofthe object, and/or a relative speed between the vehicle and the object),that is associated with an object type of the object, of 6 seconds (orany other suitable amount of time, e.g. 4 seconds, 8 seconds, etc.).Based on determining that the object is within the distance threshold(after 4 seconds, the vehicle 102 will have traversed 40 meters, theobject will have traversed 20 meters, and the vehicle will be adjacentto the object), the vehicle computing device(s) 112 can determine thelane bias region 614.

As discussed above, the distance threshold can be based on the objecttype. By way of example and without limitation, the distance thresholdassociated with a vehicle object type can be 20 meters (or any othersuitable distance, e.g., 15 meters, 10 meters, etc.) and the distancethreshold associated with a static object type (e.g., a tree branch, atipped over trash can, and the like) can be 10 meters (or any othersuitable distance, e.g., 20 meters, 30 meters, etc.). In some examples,the distance threshold can be based on a direction relative to thevehicle 102. By way of example and without limitation, the distancethreshold associated with a pedestrian that is ahead of the vehicle 102can be 30 meters (or any other suitable distance, e.g., 20 meters, 10meters, etc.) and the distance threshold associated with a pedestrianthat is behind the vehicle 102 can be 0 meters (or any other suitabledistance, e.g., 5 meters, 10 meters, etc.). In such examples, additionalcomputational resources may be freed with respect to those objectsbehind the vehicle. Similarly, and by way of example and withoutlimitation, the distance threshold associated with a bicyclist that issubstantially lateral to the vehicle 102 can be 3 meters (or any othersuitable distance, e.g., 5 meters, 10 meters, etc.). As discussed above,the vehicle computing device(s) 112 can determine that at least aportion of the object is within the distance threshold, satisfying adistance threshold condition. Upon satisfying the distance thresholdcondition, the vehicle computing device(s) 112 can determine the lanebias region 614.

In some examples and as discussed above, the vehicle computing device(s)112 can determine a velocity and/or a trajectory associated with theobject. In some examples, determining the velocity of the object maycomprise determining a relative velocity of the object based at least inpart on a velocity of the vehicle and/or a vehicle body coordinate frameor vehicle trajectory coordinate frame. and the vehicle computingdevice(s) 112 may use the object velocity and/or velocity associatedwith the vehicle 102 to determine a probability that the object will beadjacent to the vehicle 102 within a period of time. For example, theprobability can be determined by predicting a location of the vehicle102 within a period of time and predicting a location of the bicyclistwithin the period of time based at least in part on a velocity of thevehicle 102 and bicyclist, respectively, based on map data, based onpredicted trajectories of objects, and the like.

In some examples, as discussed above, the vehicle computing device(s)112 can, using the velocity of the vehicle 102 and the velocity of theobject, determine a location 632 at which the vehicle 102 is likely tobe adjacent to the object within a period of time. In some instance, thelocation 632 can be a relative location that is relative to the vehicle102 and/or the object. In some instances, the location 632 can be arange of locations that are associated with the period of time. By wayof example and without limitation, the vehicle computing device(s) 112can determine a location 632 in the environment 620 at which at least aportion of the reference trajectory 616 is within a threshold distanceof the object and/or a projected position associated with the object,which may be determined based at least in part on the object velocity.Determining such a location 632 may be based at least in part on areceding horizon (e.g., determining whether such a location existswithin a next 5 meters, 20 meters, 50 meters, 0.5 seconds of operation,3 seconds of operation). In some examples, determining the location 632may additionally or alternatively comprise determining a probabilitythat the object will be adjacent to the vehicle 102 at the location 632within the period of time or the distance specified by the recedinghorizon.

In some examples, the vehicle computing device(s) 112 can determine anestimated or multiple estimated trajectories associated with thebicyclist to determine the probability or multiple probabilities offollowing the different estimated trajectories. For example, atrajectory can indicate a path and/or an intended path for the vehicle102 and/or the object. In some instances, the estimated trajectory canbe based on map data where a higher number of road intersections,bicycle trail intersections, and the like along a portion of thetrajectory of the vehicle 102 can decrease the probability that that thebicyclist will be adjacent to the vehicle 102 within a period of time ora distance as the estimated trajectory can include a turn at anintersection and deviate from the trajectory of the vehicle 102. In suchexamples, the vehicle 102 may only bias against the object where thereis a high likelihood that the vehicle may interact with the objectthereby optimizing the safe operation of the vehicle 102 by only biasingaway when necessary for additional safety of the object.

By way of another example and without limitation, the estimatedtrajectory can be based on the sensor data 110. The vehicle computingdevice(s) 112 can detect, using the sensor data 110, an obstruction inthe road ahead and determine an estimated trajectory associated with thebicyclist that includes a stopping trajectory for the bicyclist to avoida collision. In turn, this estimate may increase the probability thatthe bicyclist will be adjacent (e.g., within a threshold distance) tothe vehicle 102. In some examples, a change in the relative velocity canincrease or decrease the probability that the bicyclist will be adjacentto the vehicle 102. By way of example and without limitation, a decreasein the velocity of the bicyclist can increase the relative velocity ofthe vehicle 102 to the bicyclist and, in turn, increase the probabilitythat the bicyclist will be adjacent to the vehicle 102. Similarly, anincrease in the velocity of the bicyclist can decrease the relativevelocity of the vehicle 102 to the bicyclist, and likewise, decrease theprobability that the bicyclist will be adjacent to the vehicle. Further,in such examples where the object trajectory swerves (e.g., to avoid apothole or other collision), any such lane biasing described herein maybe with respect to a trajectory of the object (thereby ensuringsufficient space for the object despite the object's relative motion).

In some examples, the vehicle computing device(s) 112 can use aprobability threshold to determine the lane bias region 614. Forexample, the vehicle computing device(s) 112 can determine that aprobability threshold of 50% is satisfied by determining that thebicyclist has an 80% probability of being adjacent to the vehicle 102.Based at least in part on determining that the probability satisfies theprobability threshold, the vehicle computing device(s) 112 may determinethe lane bias region 614.

The vehicle computing device(s) 112 can determine a width of the lanebias region 614 based on the object type. By way of example and withoutlimitation, the width of the lane bias region 614 can be 0.75 meters fora vehicle, 0.5 meters for a static object, 1 meter for a bicyclist, and1.5 meters for a pedestrian. As discussed above, the vehicle 102 candetermine a drivable area 610 that represents a region in theenvironment 620 where the vehicle can travel. The lane bias region 614can represent a portion of the drivable area 610 and, in some examples,represents a virtual boundary of the drivable area 610. Therefore, byway of example and without limitation, the vehicle computing device(s)112 can detect a bicyclist and determine that the lane bias region 614associated with the bicyclist can have a width of 1 meter andeffectively reduce a width of the drivable area 610 by 1 meter.

In some examples, the width of the lane bias region 614 can be based onthe width of the drivable area 610. By way of example and withoutlimitation, the drivable area 610 can have a drivable area width of 2.2meters and the vehicle 102 can have a vehicle width of 2 meters. Thevehicle computing device(s) 112 can detect a bicyclist and determinethat the lane bias region 614 associated with the bicyclist should havea width of 1 meter. However, the available width within the drivablearea 610 is 0.2 meters (the difference between the drivable area widthand the vehicle width). Therefore, the vehicle computing device(s) 112can determine that the lane bias region 614 can have a width of 0.2meters.

The vehicle computing device(s) 112 can determine a transition area fromthe drivable area 610 before being reduced by, or a portion of, the lanebias region 614 and the drivable area 610 after being reduced by, or aportion of, the lane bias region 614. For example, the vehicle computingdevice(s) 112 can use a transition distance threshold that can indicatea transition distance 634. By way of example and without limitation, thetransition threshold can indicate a transition distance of 5 meters. Adrivable area 610 can have a width of 2.5 meters and the lane biasregion 614 can have a width of 0.35 meters. The transition distance 634can indicate that the drivable area 610 can transition from 2.5 metersto a reduced drivable area (also referred to as a second drivable area)of 2.15 meters within the transition distance of 5 meters.

In some instances, the transition area between the drivable area and thereduced drivable area can based on a linear transition, an exponentialtransition, or a logarithmic transition, although other suitablecalculations (and resulting shaped areas) are contemplated. For example,a linear transition can result in a transition area that reduceslinearly with respect to the transition distance 634 and an exponentialtransition can result in a transition area that reduces exponentiallywith respect to the transition distance 634. Of course, though describedas a distinct area, such a transition area may be the area in which thevehicle transitions from the first reference line (of the lane) to thebiased position in the lane (e.g., which may be determined based oncosts for each component and may result in such linear, exponential,etc. reductions). In some instances, the transition distance 634 can bebased on the relative velocity between the vehicle 102 and the object.For example, a higher relative velocity can be associated with a longertransition distance 634 and a lower relative velocity can be associatedwith a shorter transition distance 634. Additionally or alternatively, atransition time can be used to determine the transition from thedrivable area 610 to the reduced drivable area. By way of example andwithout limitation, a time threshold associated with the transitiondistance 634 can be 10 seconds and can indicate that the vehicle 102 cantransition (via a transition area) from using the drivable area to usingthe reduced drivable area within 10 seconds.

The techniques described herein to generate the lane bias region 614 canimprove a functioning of a computing device by reducing an amount ofcomputational resources required to determine the lane bias region. Forexample, the distance threshold can allow the vehicle computingdevice(s) 112 to determine lane bias regions associated with objectsthat are within a vicinity of the vehicle 102, while preservingcomputational resources when no objects are present. Additionally,determining a probability of the object being adjacent to the vehicle102 and determining lane bias regions based on the probability can alsoreduce the amount of computational resources required to determine thelane bias region, as above. The distance threshold and the probabilitythreshold can prevent and/or limit the amount of lane bias regionsassociated with objects of environment 620 that the vehicle computingdevice(s) determines which can reduce the amount of processing power andmemory consumed by the vehicle computing device(s) 112. These and otherimprovements to the functioning of the computer are discussed herein.

As discussed above, in the case where the vehicle is an autonomousvehicle, a computing device can determine the lane bias region which canresult in providing additional space for an object to traverse theenvironment. By determining the lane bias region, the vehicle candetermine a target trajectory that reduces a probability of a negativesafety outcome (e.g., a collision) with the object and provide a morecomfortable ride for a passenger of the vehicle. Thus, using the lanebias region, the trajectory can be a safer and/or a more comfortabletrajectory than a target trajectory that does not use the lane biasregion.

FIG. 7 depicts a block diagram of an example system 700 for implementingthe techniques described herein. In at least one example, the system 700can include a vehicle 702.

The vehicle 702 can include vehicle computing device(s) 704, one or moresensor systems 706, one or more emitters 708, one or more communicationconnections 710, at least one direct connection 712, and one or moredrive systems 714.

The vehicle computing device(s) 704 can include one or more processors716 and memory 718 communicatively coupled with the one or moreprocessors 716. In the illustrated example, the vehicle 702 is anautonomous vehicle; however, the vehicle 702 could be any other type ofvehicle. In the illustrated example, the memory 718 of the vehiclecomputing device(s) 704 stores a localization component 720, aperception component 722, one or more maps 724, one or more systemcontrollers 726, and a planning component 728 comprising the drivablearea component 114, the trajectory generation component 116, the cost(s)component 118, a safety region component 730, a trajectory samplingcomponent 732, and a weight(s) component 734. Though depicted in FIG. 7as residing in memory 718 for illustrative purposes, it is contemplatedthat the localization component 720, the perception component 722, theone or more maps 724, the one or more system controllers 726, theplanning component 728, the drivable area component 114, the trajectorygeneration component 116, the cost(s) component 118, the safety regioncomponent 730, the trajectory sampling component 732, and the weight(s)component 734 may additionally, or alternatively, be accessible to thevehicle 702 (e.g., stored remotely).

In at least one example, the localization component 720 can includefunctionality to receive data from the sensor system(s) 706 to determinea position and/or orientation of the vehicle 702 (e.g., one or more ofan x-, y-, z-position, roll, pitch, or yaw). For example, thelocalization component 720 can include and/or request/receive a map ofan environment and can continuously determine a location and/ororientation of the autonomous vehicle within the map. In some instances,the localization component 720 can utilize SLAM (simultaneouslocalization and mapping), CLAMS (calibration, localization and mapping,simultaneously), relative SLAM, bundle adjustment, non-linear leastsquares optimization, or the like to receive image data, LIDAR data,RADAR data, IMU data, GPS data, wheel encoder data, and the like toaccurately determine a location of the autonomous vehicle. In someinstances, the localization component 720 can provide data to variouscomponents of the vehicle 702 to determine an initial position of anautonomous vehicle for generating a trajectory and/or for generating mapdata, as discussed herein.

In some instances, the perception component 722 can includefunctionality to perform object detection, segmentation, and/orclassification. In some examples, the perception component 722 canprovide processed sensor data that indicates a presence of an entitythat is proximate to the vehicle 702 and/or a classification of theentity as an entity type (e.g., car, pedestrian, cyclist, animal,building, tree, road surface, curb, sidewalk, unknown, etc.). Inadditional or alternative examples, the perception component 722 canprovide processed sensor data that indicates one or more characteristicsassociated with a detected entity (e.g., a tracked object) and/or theenvironment in which the entity is positioned. In some examples,characteristics associated with an entity can include, but are notlimited to, an x-position (global and/or local position), a y-position(global and/or local position), a z-position (global and/or localposition), an orientation (e.g., a roll, pitch, yaw), an entity type(e.g., a classification), a velocity of the entity, an acceleration ofthe entity, an extent of the entity (size), etc. Characteristicsassociated with the environment can include, but are not limited to, apresence of another entity in the environment, a state of another entityin the environment, a time of day, a day of a week, a season, a weathercondition, an indication of darkness/light, etc.

The memory 718 can further include one or more maps 724 that can be usedby the vehicle 702 to navigate within the environment. For the purposeof this discussion, a map can be any number of data structures modeledin two dimensions, three dimensions, or N-dimensions that are capable ofproviding information about an environment, such as, but not limited to,topologies (such as intersections), streets, mountain ranges, roads,terrain, and the environment in general. In some instances, a map caninclude, but is not limited to: texture information (e.g., colorinformation (e.g., RGB color information, Lab color information, HSV/HSLcolor information), and the like), intensity information (e.g., LIDARinformation, RADAR information, and the like); spatial information(e.g., image data projected onto a mesh, individual “surfels” (e.g.,polygons associated with individual color and/or intensity)),reflectivity information (e.g., specularity information,retroreflectivity information, BRDF information, BSSRDF information, andthe like). In one example, a map can include a three-dimensional mesh ofthe environment. In some instances, the map can be stored in a tiledformat, such that individual tiles of the map represent a discreteportion of an environment, and can be loaded into working memory asneeded, as discussed herein. In at least one example, the one or moremaps 724 can include at least one map (e.g., images and/or a mesh). Insome examples, the vehicle 702 can be controlled based at least in parton the maps 724. That is, the maps 724 can be used in connection withthe localization component 720, the perception component 722, and/or theplanning component 728 to determine a location of the vehicle 702,identify objects in an environment, and/or generate routes and/ortrajectories to navigate within an environment.

In some examples, the one or more maps 724 can be stored on a remotecomputing device(s) (such as the computing device(s) 738) accessible vianetwork(s) 736. In some examples, multiple maps 724 can be stored basedon, for example, a characteristic (e.g., type of entity, time of day,day of week, season of the year, etc.). Storing multiple maps 724 canhave similar memory requirements, but increase the speed at which datain a map can be accessed.

In at least one example, the vehicle computing device(s) 704 can includeone or more system controllers 726, which can be configured to controlsteering, propulsion, braking, safety, emitters, communication, andother systems of the vehicle 702. These system controller(s) 726 cancommunicate with and/or control corresponding systems of the drivesystem(s) 714 and/or other components of the vehicle 702.

In general, the planning component 728 can determine a path for thevehicle 702 to follow to traverse through an environment. For example,the planning component 728 can determine various routes and trajectoriesand various levels of detail. For example, the planning component 728can determine a route to travel from a first location (e.g., a currentlocation) to a second location (e.g., a target location). For thepurpose of this discussion, a route can be a sequence of waypoints fortravelling between two locations. As non-limiting examples, waypointsinclude streets, intersections, global positioning system (GPS)coordinates, distributed points along a line connecting the firstlocation to the second location constrained by a map and/or mapinformation, etc. Further, the planning component 728 can generate aninstruction for guiding the autonomous vehicle along at least a portionof the route from the first location to the second location. In at leastone example, the planning component 728 can determine how to guide theautonomous vehicle from a first waypoint in the sequence of waypoints toa second waypoint in the sequence of waypoints. In some examples, theinstruction can be a trajectory, or a portion of a trajectory. In someexamples, multiple trajectories can be substantially simultaneouslygenerated (e.g., within technical tolerances) in accordance with areceding horizon technique, wherein one of the multiple trajectories isselected for the vehicle 702 to navigate.

In some instances, the planning component 728 can include a predictioncomponent to generate predicted trajectories of objects in anenvironment. For example, a prediction component can generate one ormore predicted trajectories for vehicles, pedestrians, animals, and thelike within a threshold distance from the vehicle 702. In someinstances, a prediction component can measure a trace of an object andgenerate a trajectory for the object based on observed and predictedbehavior. Examples of generating predicted trajectories are discussed inU.S. patent application Ser. No. 16/151,607, filed Oct. 4, 2018 and Ser.No. 15/982,658, filed May 17, 2018. Application Ser. Nos. 16/151,607 and15/982,658 are herein incorporated by reference, in their entirety.

In some instances, the drivable area component 114 can includefunctionality to generate and/or determine a drivable area in anenvironment. For example, the drivable area component 114 can receivesensor data from the sensor system(s) 706 and/or can receive informationabout obstacles and/or objects in the environment from the perceptioncomponent 722. Based at least in part on the sensor data and/or oninformation associated with the objects (e.g., location, pose, extent,classification, velocity, predicted trajectories, etc.) the drivablearea component 114 can determine a drivable area comprising one or moreregions, including but not limited to a dilated region, a collisionregion, and a safety region, as discussed herein. In some instances, thedrivable area component 114 can determine an extent of the region(s)based on a classification type associated with objects proximate to theregion(s) and/or based on a velocity of the vehicle 702. Additionaldetails of the drivable area component 114 are discussed throughout thisdisclosure.

In some instances, the trajectory generation component 116 can includefunctionality to generate a reference trajectory and/or a targettrajectory within the drivable area. For example, the trajectorygeneration component 116 can receive or determine a referencetrajectory, which can correspond to a centerline of a road segment orother path through an environment. In some instances, the trajectorygeneration component 116 can generate segments that can correspond to amotion primitive generated in accordance with the techniques discussedin U.S. patent application Ser. No. 15/843,596, filed Dec. 15, 2017.Further, a segment defined by a desired curvature can be generated inaccordance with the techniques discussed in U.S. patent application Ser.No. 15/843,512, filed Dec. 15, 2017. Application Ser. Nos. 15/843,596and 15/843,512 are herein incorporated by reference, in their entirety.Additional details of the trajectory generation component 116 arediscussed throughout this disclosure.

In some instances, the cost(s) component 118 can include functionalityto evaluate one or more costs to generate a target trajectory withrespect to the reference trajectory. As discussed above, the one or morecosts may include, but is not limited to, a reference cost, an obstaclecost, a lateral cost, and longitudinal cost, and the like. In someexamples, one or more costs can be evaluated in accordance with thetechniques discussed in U.S. patent application Ser. No. 16/147,492,filed Sep. 28, 2018, which is hereby incorporated by reference, in itsentirety. Additional details of the cost(s) component 118 are discussedthroughout this disclosure.

In some instances, the safety region component 730 can includefunctionality to generate an updated safety region based at least inpart on a classification of objects in an environment and/or on avelocity of the vehicle 702 traversing the environment. As illustratedin FIGS. 5A, 5B, and 6, the safety region component 730 can determine asize of a safety region (or other region), which can be used by thecost(s) component 118 in generating a target trajectory. Additionaldetails of the safety region component 730 are discussed throughout thisdisclosure.

In some instances, the trajectory sampling component 732 can includefunctionality to determine a point density for portions of the referencetrajectory based at least in part on a cost associated with a curvaturevalue associated with a portion of the reference trajectory and/or basedat least in part on a cost associated with a distance between a point onthe reference trajectory and a point associated with an obstacle in theenvironment. As illustrated in FIG. 2, the trajectory sampling component732 can determine a density of points for a reference trajectory when acost associated with a curvature value is relatively high and/or whencost associated with a distance between a point on the referencetrajectory and a point associated with an obstacle in the environment isrelatively high (e.g., indicating that an object in the environment islocated relatively close to a reference trajectory). Increasing thenumber of points around a region of high activity or a region ofinterest allows for cost(s) to be evaluated at more points, therebyensuring a safe and comfortable ride for the vehicle 702. Additionaldetails of the trajectory sampling component 732 are discussedthroughout this disclosure.

In some instances, the weight(s) component 734 can include functionalityto select or determine weight(s) associated with evaluating one or morecost functions when generating a target trajectory. For example, and asillustrated in FIGS. 3 and 4, an action start (such as the start of adouble-parked vehicle action or a lane change action) can vary a weightof a cost in order to allow the planning component 728 generate a targettrajectory with more flexibility for the vehicle 702 to traverse theenvironment. Further, weight(s) can be adjusted in combination withother operations (e.g., when expanding a lane boundary based on aproximity of a bicyclist, bike lane, parking lane, parked car, and thelike). Additional details of the weight(s) component 734 are discussedthroughout this disclosure.

In some instances, aspects of some or all of the components discussedherein can include any models, algorithms, and/or machine learningalgorithms. For example, in some instances, the components in the memory718 (and the memory 742, discussed below) can be implemented as a neuralnetwork.

As described herein, an exemplary neural network is a biologicallyinspired algorithm which passes input data through a series of connectedlayers to produce an output. Each layer in a neural network can alsocomprise another neural network, or can comprise any number of layers(whether convolutional or not). As can be understood in the context ofthis disclosure, a neural network can utilize machine learning, whichcan refer to a broad class of such algorithms in which an output isgenerated based on learned parameters.

Although discussed in the context of neural networks, any type ofmachine learning can be used consistent with this disclosure. Forexample, machine learning algorithms can include, but are not limitedto, regression algorithms (e.g., ordinary least squares regression(OLSR), linear regression, logistic regression, stepwise regression,multivariate adaptive regression splines (MARS), locally estimatedscatterplot smoothing (LOESS)), instance-based algorithms (e.g., ridgeregression, least absolute shrinkage and selection operator (LASSO),elastic net, least-angle regression (LARS)), decisions tree algorithms(e.g., classification and regression tree (CART), iterative dichotomiser3 (ID3), Chi-squared automatic interaction detection (CHAD), decisionstump, conditional decision trees), Bayesian algorithms (e.g., naïveBayes, Gaussian naïve Bayes, multinomial naïve Bayes, averageone-dependence estimators (AODE), Bayesian belief network (BNN),Bayesian networks), clustering algorithms (e.g., k-means, k-medians,expectation maximization (EM), hierarchical clustering), associationrule learning algorithms (e.g., perceptron, back-propagation, hopfieldnetwork, Radial Basis Function Network (RBFN)), deep learning algorithms(e.g., Deep Boltzmann Machine (DBM), Deep Belief Networks (DBN),Convolutional Neural Network (CNN), Stacked Auto-Encoders),Dimensionality Reduction Algorithms (e.g., Principal Component Analysis(PCA), Principal Component Regression (PCR), Partial Least SquaresRegression (PLSR), Sammon Mapping, Multidimensional Scaling (MDS),Projection Pursuit, Linear Discriminant Analysis (LDA), MixtureDiscriminant Analysis (MDA), Quadratic Discriminant Analysis (QDA),Flexible Discriminant Analysis (FDA)), Ensemble Algorithms (e.g.,Boosting, Bootstrapped Aggregation (Bagging), AdaBoost, StackedGeneralization (blending), Gradient Boosting Machines (GBM), GradientBoosted Regression Trees (GBRT), Random Forest), SVM (support vectormachine), supervised learning, unsupervised learning, semi-supervisedlearning, etc.

Additional examples of architectures include neural networks such asResNet50, ResNet101, VGG, DenseNet, PointNet, and the like.

In at least one example, the sensor system(s) 706 can include LIDARsensors, RADAR sensors, ultrasonic transducers, sonar sensors, locationsensors (e.g., GPS, compass, etc.), inertial sensors (e.g., inertialmeasurement units (IMUs), accelerometers, magnetometers, gyroscopes,etc.), cameras (e.g., RGB, IR, intensity, depth, etc.), time of flightsensors, microphones, wheel encoders, environment sensors (e.g.,temperature sensors, humidity sensors, light sensors, pressure sensors,etc.), etc. The sensor system(s) 706 can include multiple instances ofeach of these or other types of sensors. For instance, the LIDAR sensorscan include individual LIDAR sensors located at the corners, front,back, sides, and/or top of the vehicle 702. As another example, thecamera sensors can include multiple cameras disposed at variouslocations about the exterior and/or interior of the vehicle 702. Thesensor system(s) 706 can provide input to the vehicle computingdevice(s) 704. Additionally or alternatively, the sensor system(s) 706can send sensor data, via the one or more networks 736, to the one ormore computing device(s) 738 at a particular frequency, after a lapse ofa predetermined period of time, in near real-time, etc.

The vehicle 702 can also include one or more emitters 708 for emittinglight and/or sound, as described above. The emitters 708 in this exampleinclude interior audio and visual emitters to communicate withpassengers of the vehicle 702. By way of example and not limitation,interior emitters can include speakers, lights, signs, display screens,touch screens, haptic emitters (e.g., vibration and/or force feedback),mechanical actuators (e.g., seatbelt tensioners, seat positioners,headrest positioners, etc.), and the like. The emitters 708 in thisexample also include exterior emitters. By way of example and notlimitation, the exterior emitters in this example include lights tosignal a direction of travel or other indicator of vehicle action (e.g.,indicator lights, signs, light arrays, etc.), and one or more audioemitters (e.g., speakers, speaker arrays, horns, etc.) to audiblycommunicate with pedestrians or other nearby vehicles, one or more ofwhich comprising acoustic beam steering technology.

The vehicle 702 can also include one or more communication connection(s)710 that enable communication between the vehicle 702 and one or moreother local or remote computing device(s). For instance, thecommunication connection(s) 710 can facilitate communication with otherlocal computing device(s) on the vehicle 702 and/or the drive system(s)714. Also, the communication connection(s) 710 can allow the vehicle tocommunicate with other nearby computing device(s) (e.g., other nearbyvehicles, traffic signals, etc.). The communications connection(s) 710also enable the vehicle 702 to communicate with a remote teleoperationscomputing device or other remote services.

The communications connection(s) 710 can include physical and/or logicalinterfaces for connecting the vehicle computing device(s) 704 to anothercomputing device or a network, such as network(s) 736. For example, thecommunications connection(s) 710 can enable Wi-Fi-based communicationsuch as via frequencies defined by the IEEE 802.11 standards, shortrange wireless frequencies such as Bluetooth, cellular communication(e.g., 2G, 3G, 4G, 4G LTE, 5G, etc.) or any suitable wired or wirelesscommunications protocol that enables the respective computing device tointerface with the other computing device(s).

In at least one example, the vehicle 702 can include one or more drivesystems 714. In some examples, the vehicle 702 can have a single drivesystem 714. In at least one example, if the vehicle 702 has multipledrive systems 714, individual drive systems 714 can be positioned onopposite ends of the vehicle 702 (e.g., the front and the rear, etc.).In at least one example, the drive system(s) 714 can include one or moresensor systems to detect conditions of the drive system(s) 714 and/orthe surroundings of the vehicle 702. By way of example and notlimitation, the sensor system(s) can include one or more wheel encoders(e.g., rotary encoders) to sense rotation of the wheels of the drivesystems, inertial sensors (e.g., inertial measurement units,accelerometers, gyroscopes, magnetometers, etc.) to measure orientationand acceleration of the drive system, cameras or other image sensors,ultrasonic sensors to acoustically detect objects in the surroundings ofthe drive system, LIDAR sensors, RADAR sensors, etc. Some sensors, suchas the wheel encoders can be unique to the drive system(s) 714. In somecases, the sensor system(s) on the drive system(s) 714 can overlap orsupplement corresponding systems of the vehicle 702 (e.g., sensorsystem(s) 706).

The drive system(s) 714 can include many of the vehicle systems,including a high voltage battery, a motor to propel the vehicle, aninverter to convert direct current from the battery into alternatingcurrent for use by other vehicle systems, a steering system including asteering motor and steering rack (which can be electric), a brakingsystem including hydraulic or electric actuators, a suspension systemincluding hydraulic and/or pneumatic components, a stability controlsystem for distributing brake forces to mitigate loss of traction andmaintain control, an HVAC system, lighting (e.g., lighting such ashead/tail lights to illuminate an exterior surrounding of the vehicle),and one or more other systems (e.g., cooling system, safety systems,onboard charging system, other electrical components such as a DC/DCconverter, a high voltage junction, a high voltage cable, chargingsystem, charge port, etc.). Additionally, the drive system(s) 714 caninclude a drive system controller which can receive and preprocess datafrom the sensor system(s) and to control operation of the variousvehicle systems. In some examples, the drive system controller caninclude one or more processors and memory communicatively coupled withthe one or more processors. The memory can store one or more componentsto perform various functionalities of the drive system(s) 714.Furthermore, the drive system(s) 714 also include one or morecommunication connection(s) that enable communication by the respectivedrive system with one or more other local or remote computing device(s).

In at least one example, the direct connection 712 can provide aphysical interface to couple the one or more drive system(s) 714 withthe body of the vehicle 702. For example, the direct connection 712 canallow the transfer of energy, fluids, air, data, etc. between the drivesystem(s) 714 and the vehicle. In some instances, the direct connection712 can further releasably secure the drive system(s) 714 to the body ofthe vehicle 702.

In some examples, the vehicle 702 can send sensor data to one or morecomputing device(s) 738 via the network(s) 736. In some examples, thevehicle 702 can send raw sensor data to the computing device(s) 738. Inother examples, the vehicle 702 can send processed sensor data and/orrepresentations of sensor data to the computing device(s) 738. In someexamples, the vehicle 702 can send sensor data to the computingdevice(s) 738 at a particular frequency, after a lapse of apredetermined period of time, in near real-time, etc. In some cases, thevehicle 702 can send sensor data (raw or processed) to the computingdevice(s) 738 as one or more log files.

The computing device(s) 738 can include processor(s) 740 and a memory742 storing a planning component 744.

In some instances, the planning component 744 can substantiallycorrespond to the planning component 728 and can include functionalityto generate trajectories for the vehicle 702 in an environment.

The processor(s) 716 of the vehicle 702 and the processor(s) 740 of thecomputing device(s) 738 can be any suitable processor capable ofexecuting instructions to process data and perform operations asdescribed herein. By way of example and not limitation, the processor(s)716 and 740 can comprise one or more Central Processing Units (CPUs),Graphics Processing Units (GPUs), or any other device or portion of adevice that processes electronic data to transform that electronic datainto other electronic data that can be stored in registers and/ormemory. In some examples, integrated circuits (e.g., ASICs, etc.), gatearrays (e.g., FPGAs, etc.), and other hardware devices can also beconsidered processors in so far as they are configured to implementencoded instructions.

Memory 718 and 742 are examples of non-transitory computer-readablemedia. The memory 718 and 742 can store an operating system and one ormore software applications, instructions, programs, and/or data toimplement the methods described herein and the functions attributed tothe various systems. In various implementations, the memory can beimplemented using any suitable memory technology, such as static randomaccess memory (SRAM), synchronous dynamic RAM (SDRAM),nonvolatile/Flash-type memory, or any other type of memory capable ofstoring information. The architectures, systems, and individual elementsdescribed herein can include many other logical, programmatic, andphysical components, of which those shown in the accompanying figuresare merely examples that are related to the discussion herein.

In some instances, the memory 718 and 742 can include at least a workingmemory and a storage memory. For example, the working memory may be ahigh-speed memory of limited capacity (e.g., cache memory) that is usedfor storing data to be operated on by the processor(s) 716 and 740. Insome instances, the memory 718 and 742 can include a storage memory thatmay be a lower-speed memory of relatively large capacity that is usedfor long-term storage of data. In some cases, the processor(s) 716 and740 cannot operate directly on data that is stored in the storagememory, and data may need to be loaded into a working memory forperforming operations based on the data, as discussed herein.

It should be noted that while FIG. 7 is illustrated as a distributedsystem, in alternative examples, components of the vehicle 702 can beassociated with the computing device(s) 738 and/or components of thecomputing device(s) 738 can be associated with the vehicle 702. That is,the vehicle 702 can perform one or more of the functions associated withthe computing device(s) 738, and vice versa.

FIGS. 8 and 9 illustrate example processes in accordance withembodiments of the disclosure. These processes are illustrated aslogical flow graphs, each operation of which represents a sequence ofoperations that can be implemented in hardware, software, or acombination thereof. In the context of software, the operationsrepresent computer-executable instructions stored on one or morecomputer-readable storage media that, when executed by one or moreprocessors, perform the recited operations. Generally,computer-executable instructions include routines, programs, objects,components, data structures, and the like that perform particularfunctions or implement particular abstract data types. The order inwhich the operations are described is not intended to be construed as alimitation, and any number of the described operations can be combinedin any order and/or in parallel to implement the processes.

FIG. 8 depicts an example process 800 for determining a point densityfor points associated with a reference trajectory and evaluating costsat the points to generate a target trajectory, in accordance withembodiments of the disclosure. For example, some or all of the process800 can be performed by one or more components in FIG. 1 or 7, asdescribed herein. For example, some or all of the process 800 can beperformed by the vehicle computing device(s) 112 and/or the computingdevice(s) 704.

At operation 802, the process can include receiving a referencetrajectory representing an initial trajectory for an autonomous vehicleto traverse an environment. In some instances, the reference trajectorycan comprise a segment of a continuous trajectory or a piecewisetrajectory, depending on an action to be performed and a path to befollowed in an environment. In some examples, a reference trajectory canbe based at least in part on static and/or dynamic obstacles in anenvironment, a minimum or maximum longitudinal acceleration or velocity,a maximum steering angle, vehicle dynamics (e.g., a notion that thewheels do not slip, etc.).

At operation 804, the process can include determining a first pointdensity of first points associated with a first portion of the referencetrajectory. For example, the operation 804 can include determining acost associated with a curvature value associated with a portion of thereference trajectory and/or a cost associated with a distance to a pointassociated with an obstacle that is below a threshold value. In someexamples, the operation 804 can include accessing a lookup table todetermine a point density based on the cost and/or to access a functionto determine a point density. For example, a first cost below a firstthreshold (e.g., 0.1 or x) a first point density can be selected as thepoint density for a region. Further, where a second cost is above thefirst threshold (e.g., 0.1 or y) but below a second threshold (e.g., 0.5or y), a second point density can be based on linearly interpolatingbetween density values. Further, where a third cost is above the secondthreshold (e.g., 0.5 or y), a third point density can be selected as thepoint density for a region. Of course, costs can be selected in avariety of manners.

Further, in some examples, costs associated with a curvature and/ordistance can be performed by a second layer of a planner system, asdiscussed above. For example, the second layer can receive a firsttarget trajectory from the first layer of the planner system, wherebythe first target trajectory can include curvature values. Further, theoperation 804 can include evaluating the first target trajectory overtime with respect to objects in an environment to determine an initialcost associated with a distance between a point on the first targettrajectory (e.g., a reference trajectory) and a point associated with anobstacle in the environment and can subsequently determine the pointdensity based at least in part on the cost.

At operation 806, the process can include determining a second pointdensity of second points associated with a second portion of thereference trajectory. For example, the operation 806 can includedetermining a region associated with a low curvature value (e.g., acurvature value below a threshold) and/or associated with a distance toa point associated with an obstacle that meets or exceeds a thresholdvalue.

At operation 808, the process can include evaluating cost function(s) ata first point of the first points and at a second point of the secondpoints to generate a target trajectory. For example, the operation 808can include evaluating one or more of a reference cost, an obstaclecost, a lateral cost, a longitudinal cost, and the like.

At operation 810, the process can include determining whether cost(s)are below a threshold. For example, the operation 810 can include usingtechnique(s) involving a gradient descent to optimize the costfunction(s). In some examples, the operation 810 can include determiningwhether a cost is below a threshold value, determining whether a changein cost between optimization operations is below a threshold value,and/or determining whether a number of optimization iterations has beenperformed. If the costs meet or exceed a threshold (e.g., “no” in theoperation 810) the process returns to the operation 808 where anothertrajectory can be generated (e.g., by varying locations of the pointsand/or controls (e.g., steering angles, acceleration, etc.)) and updatedcosts can be determined. If the costs are below a threshold (e.g., “yes”in the operation 810), the process can continue to operation 812.

At operation 812, the process can include controlling the autonomousvehicle to traverse the environment based at least in part on the targettrajectory. In some instances, the target trajectory can be provided toa trajectory smoother component and/or the trajectory tracker componentto refine the target trajectory and/or to generate control signals forthe various motors and steering actuators of the autonomous vehicle.

FIG. 9 depicts an example process 900 for determining weights associatedwith points along a reference trajectory (e.g., those points determinedin accordance with FIG. 8) and evaluating, based on the weights, costsat the points to determine a target trajectory, in accordance withembodiments of the disclosure. For example, some or all of the process900 can be performed by one or more components in FIG. 1 or 7, asdescribed herein. For example, some or all of the process 900 can beperformed by the vehicle computing device(s) 112 and/or the computingdevice(s) 704.

At operation 902, the process can include receiving a referencetrajectory representing an initial trajectory for an autonomous vehicleto traverse an environment. In some examples, this operation 902 cansubstantially correspond to the operation 802.

At operation 904, the process can include determining portion(s) of thereference trajectory. For example, the operation 904 can includedetermining a first portion 906, a second portion 908, and an N-thportion 910. In some examples, the portions 906, 908, and 910 can bediscrete portions of (and/or points along) the reference trajectory. Insome examples, the operation 904 can include determining the portionsbased at least in part on actions or maneuvers to be performed by theautonomous vehicle. For example, an action or maneuver may include, butis not limited to, a double-parked vehicle action or a lane changeaction. In some examples, the operation 904 can include determining thatthe autonomous vehicle is proximate to a bicyclist or another object inthe environment and determining a portion of the reference trajectorybased at least in part on a predicted trajectory of the bicyclist. Insome examples, the operation 904 can include determining a locationassociated with the vehicle based on map data that may indicateweight(s) or a trigger to vary weights, as discussed herein.

At operation 912, the process can include determining weight(s)associated with reference cost(s) to be evaluated at point(s) associatedwith the portion(s) of the reference trajectory. In some cases, areference cost is associated with a difference between a point on thereference trajectory and a corresponding point. For example, theoperation 912 can include determining a first weight 914, a secondweight 916, and an M-th weight 918. In some examples, the operation 912can include determining a weight that corresponds to individual portionsas determined in the operation 904. In some examples, the operation 912can include determining a weight that corresponds to individual points(e.g., reference points) associated with individual portions. That is,if the portion 906 comprises 10 points, the operation 912 can includedetermining a unique weight for cost(s) associated with each of the 10points. Of course, other configurations are contemplated herein.

At operation 920, the process can include evaluating a reference costfunction at the point(s) to generate a target trajectory comprising thecorresponding point. In some examples, the operation 920 can includescaling a cost by a weight as determined in the operation 912. Forexample, the operation 920 can include scaling a cost up or down basedon the weight associated with the cost, which, in turn, can lead tochanges in the contour of the target trajectory based on the weightsassociated with the various costs, as discussed herein. In someinstances, the operation 920 can include evaluating other costfunctions, and is not limited to evaluating a reference costs function.Thus, the operations provide a flexible framework to generatetrajectories

At operation 922, the process can include controlling the autonomousvehicle to traverse the environment based at least in part on the targettrajectory. In some examples, this operation 922 can substantiallycorrespond to the operation 812.

FIG. 10 is an illustration of determining a cost at a location that isassociated with a portion of a trajectory, in accordance withembodiments of the disclosure.

As illustrated in FIG. 10, the vehicle 102 can traverse an environment1000 which can include a first object 1002 and a second object 1004. Thefirst object 1002 and the second object 1004 can include, but are notlimited to a static object (e.g., building, curb, sidewalk, lanemarking, sign post, traffic light, tree, etc.) or a dynamic object(e.g., a vehicle, bicyclist, pedestrian, animal, etc.). As discussedabove, the vehicle 102 can use sensor system(s) 108 to generate sensordata 110 and the vehicle computing device(s) 112 can use the sensor data110 to detect the first object 1002 that and the second object 1004 inthe environment 1000. The vehicle 102 can, based on detecting the firstobject 1002 and the second object 1004, determine a trajectory 1006 totraverse the environment 1000.

In some instances, the vehicle computing device(s) 112 associated withthe vehicle 102 can determine a cost associated with a portion of thetrajectory 1006. As discussed above, a cost can include, but is notlimited to a reference cost, an obstacle cost, a collision cost, alateral cost, a longitudinal cost, and the like. For example, a cost1008 can be based on a location 1010 associated with a portion of thetrajectory 1006 and a portion of an object (e.g., first object 1002) inthe environment 1000 including other parameters such as aprioritization, an object type of the first object 1002, a weight, etc.

In some instances, the vehicle computing device(s) 112 can determine adrivable area 1012 and a position of the first object 1002 and/or thesecond object 1004 to determine a reduced drivable area 1014 where aportion of the drivable area 1012 that excludes the reduced drivablearea 1014 can represent, for example, a collision region. As discussedabove, the reduced drivable area 1014 can represent a smaller drivablearea than the drivable area 1012 where the reduced drivable area 1014can represent an area for the vehicle 102 to avoid to further reduce alikelihood that the vehicle 102 will collide with an object in theenvironment 1000. In some examples, a cost associated with entering thecollision region can be relatively high (relative to the traversing inthe reduced drivable area 1014). In some examples, the drivable area1012 can represent an area clear of any objects and/or obstacles in theenvironment 1000. That is, the drivable area 1012 can represent portionsof the environment where the vehicle 102 can drive. The reduced drivablearea 1014 can represent a portion of the drivable area encoded withinformation about objects in the environment 1000 and used for variouscosts. In some examples, the reduced drivable area 1014 can representoffsets from objects in the environment 1000 (where the offsets can bedynamically determined) for the purpose of determining a cost associatedwith a collision (e.g., a collision cost).

A portion of the object can correspond to a point on a boundary such asa boundary of a drivable area 1012 or a virtual boundary 1016. Asdiscussed above, a virtual boundary can be determined based oninformation about the objects in the environment 1000, which can includeinformation about semantic classifications and/or probabilistic models.In at least some examples, the virtual boundary 1016 can be encoded withinformation (such as lateral distance to the nearest object, semanticclassification of the nearest object, related probabilities of suchdistance and classification, etc.). In some instances, the virtualboundary 1016 can represent a boundary of the reduced drivable area 1014and/or a collision region.

The vehicle computing device(s) 112 can determine a width 1018associated with a portion of the drivable area 1012. The width 1018 canrepresent a width available to the vehicle 102 to traverse through theavailable drivable area 1012 that can be associated with a distancebetween a virtual boundary 1016 and/or a distance between objects in theenvironment 1000. In some instances, the vehicle computing device(s) 112can compare the width 1018 to a width of the vehicle 102. Based on thecomparison, the vehicle computing device(s) 112 can determine a costassociated with a portion of the trajectory 1006 that is substantiallyin between the first object 1002 and the second object 1004 or a virtualboundary 1016.

By way of example and without limitation, the vehicle computingdevice(s) 112 can determine a higher cost when a width of the vehicleexceeds the width 1018 (e.g., a blockage) and can determine a lower costwhen the width of the vehicle is less than the width 1018. As discussedabove, in some instances, the vehicle computing device(s) 112 candetermine a location 1010 and a cost associated with a portion of thetrajectory 1006. The vehicle computing device(s) 112 can determine ahigher cost when the cost 1008 between the location 1010 and a portionof an object (e.g., first object 1002) is less than a portion of a widthof the vehicle 102 and determine a lower cost when the cost 1008 exceedsa portion of the width of the vehicle 102. In some instances, thevehicle computing device(s) 112 can determine a higher cost when thewidth of the vehicle is less than the width 1018 but the vehicle 102cannot capably traverse to the location 1010 based on a pose of thevehicle 102 without interfering with an object in the environment (e.g.,a “collision), as discussed in further detail below.

By way of example and without limitation, the location 1010 can beassociated with a portion of the trajectory 1006 and the vehicle 102 cantraverse the environment 1000 and the vehicle computing device(s) 112can predict that as the vehicle 102 approaches the location 1010, thatthe location 1010 will be substantially in a middle portion of thevehicle 102 relative to a lateral dimension of the vehicle 102. Then thevehicle computing device(s) 112 can compare half of the width of thevehicle 102 (or any other suitable width, e.g., one third of the widthof the vehicle, two thirds of the width of the vehicle, half of thewidth of the vehicle including a fixed offset, etc.) with the cost 1008to determine the cost associated with the location 1010.

The vehicle computing device(s) 112 can compare the cost with a costthreshold to determine a vehicle action (also referred to as maneuversor vehicle maneuvers). For example, the cost can exceed a cost thresholdand the vehicle computing device(s) 112 can determine a stop actionassociated with the location 1010, although any suitable vehicle actionsare contemplated, e.g., a follow action, a slow action, etc. Examples ofvehicle actions (or maneuvers) can be found, for example, in U.S. patentapplication Ser. No. 16/232,863, filed Dec. 26, 2018 and U.S. patentapplication Ser. No. 16/181,164, filed Nov. 5, 2018, which are herebyincorporated by reference, in their entirety.

Based on the vehicle action, the vehicle 102 can follow the trajectory1006 and perform the vehicle action as the vehicle 102 approaches and/orarrives at the location 1010. For example, the vehicle 102 can perform astop action at or near the location 1010 to prevent a collision with thefirst object 1002 and/or the second object 1004. In some instances, thevehicle computing device(s) 112 can determine the stop action at thelocation 1010 to stop the vehicle 102 within a threshold distance to thefirst object 1002 and/or the second object 1004. In some instances, thestop action can be associated with a probability of preventing acollision with the first object 1002 and/or the second object 1004. Insome instances, the vehicle 102 can determine an object trajectoryassociated with the first object 1002 and/or the second object 1004 andperform a follow action at or near the location 1010 and wait at thelocation 1010 and follow, for example, the second object 1004 as thesecond object 1004 changes a position of the second object 1004 inaccordance with the object trajectory.

As discussed above, the first object 1002 and/or the second object 1004can change a position of the first object 1002 and/or the second object1004. In some instances, the first object 1002 and/or the second object1004 can change the position at a time prior to the vehicle 102approaching the location 1010. The vehicle computing device(s) 112 candetect the change in position and, in some instances, refrain fromperforming the action at the location 1010 and/or discard the action. Insuch examples, the cost associated with location 1010 may becontinuously computed/updated as the vehicle 102 progresses (e.g., at agiven frequency, such as, but not limited to, 10 Hz, 30 Hz, 100 Hz,etc.).

By way of example and without limitation, based on the locations of thefirst object 1002 and the second object 1004, the vehicle computingdevice(s) 112 can determine a stop action associated with the location1010 based on expected locations of the vehicle 102 and/or the objects1002 and 1004 at a future time. Then, the second object 1004 can changeits position prior to the vehicle 102 arriving at the location 1010which can increase the width 1018 to exceed the width of the vehicle102. Then the vehicle 102 can refrain from performing the stop action asthe vehicle 102 arrives at the location 1010 and continue to traversethe environment 1000 based at least in part on the trajectory 1006.However, if the environment remains the same as or similar to theconfiguration giving rise to the stop action, the vehicle 102 canimplement a stopping action to stop in accordance with the stop actionas the vehicle 102 approaches the stop action. In at least someexamples, such actions may further comprise reducing a velocity inanticipation of the location 1010 to provide additional time for thesituation to be resolved before coming to a full stop (e.g., eitherother object may move, thereby providing sufficient space for thevehicle 102 to traverse obviating the need for a full stop).

In some instances, the vehicle computing device(s) 112 can determine thelocation 1010 can be based on a capability of the vehicle 102. Forexample, the vehicle computing device(s) 112 can determine capabilitydata associated with the vehicle 102 which can indicate capabilitiessuch as an acceleration capability, a turning radius capability, and thelike. The vehicle computing device(s) 112 can, using a referencetrajectory and the capability data, determine the trajectory 1006 whichcan indicate where the vehicle 102 can capably traverse. In someexamples, various sampled points along the trajectory may be selectedfor performing such determinations. As non-limiting examples, suchdeterminations may be made for every 1 m along a trajectory, at periodiclocations where a width of the drivable surface is less than a thresholdwidth, and the like.

By way of example and without limitation, the vehicle computingdevice(s) 112 can receive a reference trajectory which can represent aninitial path or trajectory for the vehicle 102 to follow in theenvironment 1000 (which, in some instances, may comprise the center of alane or, otherwise, a preferred trajectory for the vehicle 102 tofollow). In some instances, the reference trajectory can exclude thecapability data and indicate a desired trajectory for the vehicle 102 tofollow. The vehicle computing device(s) 112 can determine capabilitydata which can be based on, for example, a current state of the vehicle102. The current state can indicate a velocity of the vehicle 102, apose of the vehicle 102, and/or other state data associated with thevehicle 102.

As discussed above, the capability data can indicate an accelerationcapability, a turning radius capability, and the like. By way of exampleand without limitation, the vehicle computing device(s) 112 candetermine that the vehicle 102 is traveling at a velocity of 15 metersper second and determine a stopping distance, based at least in part onthe velocity, of 10 meters. Similarly, the vehicle computing device(s)112 can determine a pose of the vehicle 102 and determine, based atleast in part on the turning radius capability of the capability data, aportion of the environment 1000 that the vehicle 102 can capablytraverse toward.

Therefore, as discussed above, the vehicle computing device(s) 112 can,using the capability data, determine the location 1010 which can beassociated with a portion of the environment 1000 that the vehicle 102can capably traverse. In some instances, the vehicle computing device(s)112 can determine the trajectory 1006, the location 1010, and/or thevehicle action on a continuous basis and/or on a periodic basis. Forexample, the vehicle computing device(s) 112 can determine thetrajectory 1006, the location 1010, and/or the vehicle action every 0.1seconds, although any suitable time increment is contemplated (e.g., 0.5seconds, 1 second, etc.). In some instances, the cost 1008 can be basedat least in part on the capability data. By way of example and withoutlimitation, the portions of the environment 1000 and/or the trajectory1006 that the vehicle 102 can capably traverse can be associated with alower cost than the portions of the environment 1000 and/or thetrajectory 1006 that the vehicle cannot capably traverse.

FIG. 11A is another illustration 1100 of determining cost(s) associatedwith a portion of a trajectory and/or an action based on such cost(s),in accordance with embodiments of the disclosure. As discussed above,the vehicle 102 can determine a first drivable area 1102 and detectobject 1104 a and object 1104 b in the environment. Although depicted asa vehicle, the object 1104 a and object 1104 b can be any object such asa pedestrian, a bicyclist, and the like. By way of example and withoutlimitation, the object 1104 a and object 1104 b can be static objects(e.g., a parked vehicles). The vehicle computing device(s) 112 candetermine, based on detecting the object 1104 a and object 1104 b, areduced drivable area 1106 which can include a virtual boundary thatdistinguishes the reduced drivable area 1106 from a collision region.

Based at least in part on the reduced drivable area 1104 and/or thecollision region, the vehicle computing device(s) 112 can determine atrajectory 1108 to traverse the environment. Additionally, based atleast in part on the reduced drivable area 1104 and/or the trajectory1108, the vehicle computing device(s) 112 can determine a first cost1110. In some instances, the reduced drivable area 1106 and/or the firstcost 1110 can be based on a type of the object 1104 a and/or object 1104b. In some instances, the first cost 1110 can additionally be based on adistance between the vehicle 102 and the object 1104 a and/or object1104 b. By way of example and without limitation, the first cost 1110can be a higher cost when associated with a pedestrian as compared towhen the first cost 1110 is associated with debris or another vehicle.Based at least in part on the first cost 1110 meeting or exceeding acost threshold, the vehicle computing device(s) 112 can determine a stopvehicle action (e.g., vehicle action 1112) associated with the portionof the trajectory that is associated with the first cost 1110. In someinstances, the vehicle action can represent a “subgoal” whereby thevehicle 102 can traverse along the trajectory 1108 until it reaches alocation associated with the subgoal. In some instances, the environmentmay update to remove the subgoal (e.g., an object may move or atrajectory may be updated) or the subgoal may remain and the vehicle 102can be controlled or otherwise perform the subgoal (e.g., stopping orfollowing).

FIG. 11B is another illustration 1114 of determining a cost associatedwith a portion of a trajectory, in accordance with embodiments of thedisclosure. As discussed above and similar to FIG. 11A, the vehiclecomputing device(s) 112 can determine the first drivable area 1102,detect the objects 1104 a and 1104 b, determine the reduced drivablearea 1106, and the trajectory 1108. Additionally, the vehicle computingdevice(s) 112 can detect an object 1104 c, which can be another vehicle,and an object 1104 d, which can be a pedestrian. Although depicted as avehicle and a pedestrian, respectively, the object 1104 c and object1104 d can be any suitable object. By way of example and withoutlimitation, as the vehicle 102 traverses the environment, the vehiclecomputing device(s) 112 can determine a second cost 1116 can that doesnot meet or exceed a cost threshold. Based at least in part ondetermining that the second cost 1116 does not meet or exceed the costthreshold, the vehicle computing device(s) 112 can determine a followaction (e.g., vehicle action 1118) associated with the portion of thetrajectory 1108 that is associated with the second cost 1116. By way ofexample and without limitation, the follow action can be associated withobject 1104 a such that when object 1104 a traverses through theenvironment, the vehicle 102 can follow the object 1104 a based at leastin part on the movement of object 1104 a.

Additionally, by way of example and without limitation, the vehiclecomputing device(s) 112 can further determine a third cost 1120 that canmeet or exceed the cost threshold. For example, the object 1104 d cancause the vehicle computing device(s) 112, in some instances, todetermine a third cost 1120 that is higher than the second cost 1116because the object 1104 d is associated with a pedestrian object type.Based at least in part on the third cost 1120 meeting or exceeding thecost threshold, the vehicle computing device(s) 112 can determine a stopvehicle action (e.g., vehicle action 1122) associated with the portionof the trajectory 1108 that is associated with the third cost 1120. Byway of example and without limitation, the distances between thelocation associated with the second cost 1116 and objects 1104 a and1104 b as well as the distances between and the location associated withthe third cost 1120 and the objects 1104 c and 1104 d can besubstantially similar or the same. Therefore, a difference in thevehicle action 1118 and the vehicle action 1122 can be based at least inpart on a different in the second cost 1116 and the third cost 1120, aprioritization, an object type, a weight, etc.

FIG. 11C is another illustration 1124 of determining a cost associatedwith a portion of a trajectory, in according with embodiments of thedisclosure. As discussed above and similar to FIGS. 11A and 11B, thevehicle computing device(s) 112 can determine the first drivable area1102, detect the object 1104 a, determine the reduced drivable area1106, and the trajectory 1108. In some instances, the vehicle computingdevice(s) 112 can detect an object 1104 e, which can be another vehicle,that is associated with a trajectory 1126. Although depicted as avehicle, the object 1104 e can be any object such as a pedestrian, abicyclist, and the like. In some instances, the trajectory 1126 can bean estimated trajectory based at least in part on observedcharacteristics of the object 1104 e and/or based at least in part onprojecting a location of the vehicle 102 forward in time and projectinga location of the object 1104 e forward in time. In some instances, thevehicle computing device(s) 112 can project the location of the vehicle102 and/or the object 1104 e on a periodic basis (e.g., every 0.01second, every 0.1 second, etc., although any suitable time period iscontemplated).

By way of example and without limitation, as the vehicle 102 traversesthe environment, the vehicle computing device(s) 112 can determine afourth cost 1128 that meets or exceeds a cost threshold. Based at leastin part on the fourth cost 1128 meeting or exceeding the cost thresholdand based at least in part on the trajectory 1126, the vehicle computingdevice(s) 112 can determine a follow vehicle action (e.g., vehicleaction 1130) associated with a portion of the trajectory 1108 that isassociated with the fourth cost 1128. In some instances, the followvehicle action can be based at least in part on the trajectory 1126.Using the follow vehicle cation, the vehicle 102 can follow thetrajectory 1108 to continue traversing the environment.

FIG. 12 depicts a block diagram of an example system 1200 forimplementing the techniques described herein. As described above, thesystem 1200 can include the vehicle 702 described with reference to FIG.7.

The planning component 728 can further comprise a location component1202 and an action component 1204. Though depicted in FIG. 12 asresiding in memory 718 for illustrative purposes, it is contemplatedthat the location component 1202 and the action component 1204 mayadditionally, or alternatively, be accessible to the vehicle 702 (e.g.,stored remotely).

The location component 1202 can include functionality to determine alocation of an environment that is associated with a portion of atrajectory of a vehicle. For example, the location component 1202 canreceive drivable area data from the drivable area component 114 whichcan indicate one or more regions including, but not limited to, adrivable region, a reduced drivable region, and/or a collision region,as discussed herein. Additionally, the location component 1202 canreceive a reference trajectory and/or a target trajectory within thedrivable area.

Based on the trajectory (e.g., the reference trajectory and/or thetarget trajectory) and the drivable region (e.g., the drivable region,the reduced drivable region, and/or the collision region), the locationcomponent 1202 can determine a width associated with a portion of thedrivable region. In some instances, the width can represent a distancebetween a first object and a second object, an object and a boundary ofa drivable region, and/or between a first boundary of a drivable regionand a second boundary of the drivable region.

The location component 1202 can compare the width with a width of thevehicle and determine a location that is associated with a portion ofthe trajectory based on the width associated with the portion of thedrivable region exceeding the width of the vehicle. The locationdetermined by the location component 1202 can represent a location wherethe vehicle can proceed toward but not pass without exceeding a cost(e.g., a “blockage”). For example, the cost(s) component 118 candetermine a cost associated with the location and a cost threshold.Based on determining that the cost exceeds the cost threshold, thevehicle can determine to proceed toward the location but not pass thelocation as it traverses the environment.

The action component 1204 can include functionality to determine avehicle action associated with the location. For example, the actioncomponent 1204 can determine vehicle actions that can include a stopaction or a follow action, although other suitable vehicle actions arecontemplated. A stop action can indicate that the vehicle should stop ator near the location associated with the stop action. A follow actioncan indicate that the vehicle should follow an object (e.g., anothervehicle in the environment). As discussed above, an object can change aposition and the vehicle can perform a follow action associated with theobject to maintain a distance and/or speed relative to the object to befollowed.

FIG. 13 depicts an example process 1300 for determining a cost of alocation that is associated with a portion of a trajectory andevaluating the cost to determine an action, in accordance withembodiments of the disclosure.

At operation 1302, the process 1300 can include receiving a vehicletrajectory associated with a vehicle traversing an environment. In someinstances, the trajectory can be a reference trajectory that comprises asegment of a continuous trajectory or a piecewise trajectory, dependingon an action to be performed and a path to be followed in anenvironment. In some examples, a reference trajectory can be based atleast in part on map data (e.g., a centerline of a road segment), staticand/or dynamic obstacles in an environment, a minimum or maximumlongitudinal acceleration or velocity, a maximum steering angle, vehicledynamics (e.g., a notion that the wheels do not slip, etc.).

In some instances, the trajectory can be a target trajectory that can bebased at least in part on evaluating one or more costs at points on thereference trajectory relative to the drivable area, whereby a density ofpoints on the reference trajectory and weights associated with cost(s)can be determined as discussed herein.

At operation 1304, the process 1300 can include receiving sensor datafrom a sensor associated with the vehicle. The sensor data can includeimage data, lidar data, radar data, time of flight data, sonar data, andthe like, that is associated with an environment of the vehicle.

At operation 1306, the process 1300 can include determining an objectrepresented in the sensor data. For example, the vehicle can include aperception system, which can perform object detection, segmentation,and/or classification based at least in part on the sensor data receivedfrom sensor systems to detect the object represented in the sensor data.

At operation 1308, the process 1300 can include determining a drivablearea associated with a virtual boundary. For example, the vehicle candetermine a drivable region of an environment by using data such assensor data of an environment and/or map data of an environment todetermine the drivable region of the environment. In some instances, thedrivable region can represent a region in the environment that candefine the constraints and/or boundaries (e.g., virtual boundaries)within which a vehicle can safely travel relative to the objects toeffectively reach an intended destination.

At operation 1310, the process 1300 can include determining a locationassociated with a portion of the trajectory. As discussed above, thelocation can be a location that is separated by a distance from anobject in the environment and/or a boundary of a drivable area (e.g., adrivable area, a reduced drivable area, and/or a collision region). Insome examples, the location can be determined by predicting motion ofthe vehicle along a trajectory over time. Further, position(s) ofobjects in an environment can be determined based on predicted motion ofsuch objects over time to determine relative and/or expected locationsof the vehicle and object(s) in an environment (e.g., in the future).

At operation 1312, the process 1300 can include comparing the locationwith a threshold distance. The threshold distance can be based on adimension of the vehicle (e.g., a width of the vehicle) and/or acapability of the vehicle. As discussed above, a capability of thevehicle can indicate an acceleration capability, a turning radiuscapability, and the like. If the location is not within the thresholddistance, then the process 1300 can return to operation 1310. If thelocation is within the threshold distance, then the process 1300 canproceed to operation 1314.

At operation 1314, the process 1300 can include determining an obstaclecost associated with the location. As discussed above, a cost caninclude, but is not limited to a reference cost, an obstacle cost, alateral cost, a longitudinal cost, and the like, where an obstacle costincreases when the distance that separates the location from the objectdecreases as compared to the threshold distance and where the obstaclecost decreases when the distance that separates the location from theobject increases as compared to the threshold distance.

At operation 1316, the process 1300 can include comparing the obstaclecost with a threshold cost. If the obstacle cost does not meet or exceedthe threshold cost, then the process 1300 can return to operation 1310.If the obstacle cost meets or exceeds the threshold cost, then theprocess 1300 can proceed to operation 1318.

At operation 1318, the process 1300 can include determining an action ofthe vehicle associated with the location. As discussed above, an actioncan include a stop action or a follow action, although other suitableactions are contemplated such as a slow action, yield action, etc.

At operation 1320, the process 1300 can include controlling, based atleast in part on the trajectory, the vehicle to traverse theenvironment. In some instances, the vehicle can perform the actiondetermined at operation 1318 at or near the location. In some instances,the vehicle can refrain from performing the action.

At operation 1318, the process 1300 can include determining an action ofthe vehicle associated with the location. As discussed above, an actioncan include a stop action or a follow action, although other suitableactions are contemplated.

The techniques discussed herein can improve a functioning of a computingdevice in a number of additional ways. In some cases, determining a costand a vehicle action, based on the cost, can reduce an amount ofprocessing by allowing a planner system to evaluate a predicted safetyoutcome without discarding a trajectory of the vehicle. This can allow avehicle to progress along the trajectory as it approaches a locationassociated with the vehicle action. In some examples, determiningvehicle actions associated with a cost can result in safer outcomes(e.g., by stopping a vehicle near a blockage and/or proceeding byexecuting a follow action) and/or can result in more comfortabletrajectories (e.g., by reducing unnecessary stops and/or brake taps). Insome examples, determining varying costs associated with various objectscan improve safety by generating and/or modifying the vehicle actionsand/or trajectory based on costs. Further, the techniques discussedherein can be used alone or in combination to improve safety and/orcomfort in a variety of systems that generate the vehicle actions.

EXAMPLE CLAUSES

A: A system comprising: one or more processors; and one or morecomputer-readable media storing instructions executable by the one ormore processors, wherein the instructions, when executed, cause thesystem to perform operations comprising: receiving a referencetrajectory representing an initial trajectory for an autonomous vehicleto traverse an environment; determining a first point density of firstpoints associated with a first portion of the reference trajectory;determining a second point density of second points associated with asecond portion of the reference trajectory, wherein the second pointdensity is less than the first point density; evaluating a cost functionat a first point of the first points and at a second point of the secondpoints to generate a target trajectory comprising first correspondingpoints and second corresponding points, wherein a first correspondingpoint of the first corresponding points corresponds to the first pointof the first points, and wherein a second corresponding point of thesecond corresponding points corresponds to a second point of the secondpoints; and controlling the autonomous vehicle to traverse theenvironment based at least in part on the target trajectory.

B: The system of paragraph A, wherein the first point density is based,at least in part, on one or more of: a time period between adjacentpoints of the first points; or a distance between the adjacent points ofthe first points.

C: The system of paragraph A or B, wherein the cost function comprises acost based at least in part on a distance between the firstcorresponding point of the target trajectory and a point associated withan obstacle in the environment.

D: The system of any of paragraphs A-C, wherein the first point densityis based at least in part on determining that at least one of: a firstcost associated with a curvature value associated with the first pointof the reference trajectory; or a second cost associated with a distancebetween the first point and a point associated with an obstacle in theenvironment.

E: The system of paragraph D, wherein determining the first pointdensity comprises accessing a lookup table or a function to determinethe first point density based at least in part on the first cost or thesecond cost.

F: The system of any of paragraphs A-E, wherein a first time periodassociated with the first point and an adjacent first point of the firstpoints is less than a second time period associated with the secondpoint and an adjacent second point of the second points.

G: A method comprising: receiving a first trajectory representing aninitial trajectory for a vehicle to traverse an environment; determininga point density for points associated with a portion of the firsttrajectory based at least in part on a first cost associated with acurvature value of the portion or a second cost associated with adistance between a first point of the portion and a second pointassociated with an obstacle in the environment; determining, based atleast on a cost associated with the first point, a second trajectory;and controlling the vehicle to traverse the environment based at leastin part on the second trajectory.

H: The method of paragraph G, wherein at least one of: the point densityincreases as the first cost associated with the curvature valueincreases; or the point density increases as the second cost associatedwith the distance between a first point and the second point increases.

I: The method of paragraph G or H, wherein: the point density is a firstpoint density; the portion is a first portion; the points are firstpoints; the curvature value is a first curvature value; the distance isa first distance; the obstacle is a first obstacle; and the methodfurther comprises: determining a second point density for second pointsassociated with a second portion the first trajectory based at least inpart on a third cost associated with a second curvature value of thesecond portion or a fourth cost associated with a second distancebetween a third point of the second portion and a fourth pointassociated with a second obstacle in the environment, wherein the firstpoint density is different than the second point density.

J: The method of any of paragraphs G-I, wherein the distance is a firstdistance, and wherein the point density is based at least in part on oneof: a second distance between the first point on the portion and anadjacent first point on the portion; or a time period associated with adifference in a first time associated with the first point and a secondtime associated with the adjacent first point.

K: The method of any of paragraphs G-J, wherein the cost is based atleast in part on the distance between the first point of the portion andthe second point associated with the obstacle in the environment.

L: The method of any of paragraphs G-K, wherein determining the secondtrajectory further comprises: evaluating the cost for a plurality ofpoints associated with the first trajectory to generate the secondtrajectory.

M: The method of any of paragraphs G-L, further comprising: receivingthe first trajectory from a first layer of a planning system of thevehicle, wherein the first trajectory is generated with respect to adistance between points on the first trajectory and the secondtrajectory is generated, by a second layer of the planning system, withrespect to a time period between points on the second trajectory.

N: The method of paragraph M, wherein the point density is non-uniformwith respect to the portion of the first trajectory.

O: A non-transitory computer-readable medium storing instructions that,when executed, cause one or more processors to perform operationscomprising: receiving a first trajectory representing an initialtrajectory for a vehicle to traverse an environment; determining a pointdensity for points associated with a portion of the first trajectorybased at least in part on a first cost associated with a curvature valueof the portion or a second cost associated with a distance between afirst point of the portion and a second point associated with anobstacle in the environment; generating, based at least in part on acost associated with the first point, a second trajectory, the secondtrajectory comprising a target point; and controlling the vehicle totraverse the environment based at least in part on the secondtrajectory.

P: The non-transitory computer-readable medium of paragraph O, whereinat least one of: the point density increases as the first costassociated with the curvature value increases; or the point densityincreases as the second cost associated with the distance between afirst point and the second point increases.

Q: The non-transitory computer-readable medium of paragraph O or P,wherein: the point density is a first point density; the portion is afirst portion; the points are first points; the curvature value is afirst curvature value; the distance is a first distance; the obstacle isa first obstacle; and the operations further comprise: determining asecond point density for second points associated with a second portionthe first trajectory based at least in part on a third cost associatedwith a second curvature value of the second portion or a fourth costassociated with a second distance between a third point of the secondportion and a fourth point associated with a second obstacle in theenvironment, wherein the first point density is different than thesecond point density.

R: The non-transitory computer-readable medium of any of paragraphs O-Q,wherein the distance is a first distance, and wherein the cost is basedat least in part on a second distance between the target point of thesecond trajectory and the second point associated with the obstacle inthe environment.

S: The non-transitory computer-readable medium of any of paragraphs O-R,wherein a first time period associated with the first point and anadjacent first point of the first points is less than a second timeperiod associated with the second point and an adjacent second point ofthe second points.

T: The non-transitory computer-readable medium of paragraph S, theoperations further comprising: receiving the first trajectory from afirst layer of a planning system of the vehicle, wherein the firsttrajectory is generated with respect to a Euclidian distance betweenpoints on the first trajectory and the second trajectory is generated,by a second layer of the planning system, with respect to a time periodbetween points on the second trajectory.

U: A system comprising: one or more processors; and one or morecomputer-readable media storing instructions executable by the one ormore processors, wherein the instructions, when executed, cause thesystem to perform operations comprising: receiving a referencetrajectory representing an initial path for an autonomous vehicle totraverse an environment; determining a first portion of the referencetrajectory; determining a second portion of the reference trajectorythat is separate from the first portion; determining a first weightassociated with a first reference cost to be evaluated at a first pointassociated with the first portion of the reference trajectory, whereinthe first reference cost is associated with a first difference betweenthe first point on the reference trajectory and a corresponding firstpoint; determining a second weight associated with a second referencecost to be evaluated at a second point associated with the secondportion of the reference trajectory, wherein the second reference costis associated with a second difference between the second point on thereference trajectory and a corresponding second point, wherein thesecond weight is larger than the first weight; evaluating a referencecost function at the first point and the second point to generate atarget trajectory comprising the corresponding first point and thecorresponding second point; and controlling the autonomous vehicle totraverse the environment based at least in part on the targettrajectory.

V: The system of paragraph U, wherein the first portion of the referencetrajectory is associated with at least one of a discontinuity in thereference trajectory or an obstacle cost that meets or exceeds athreshold obstacle cost.

W. The system of paragraph U or V, wherein the first differencecomprises one or more of a difference in a yaw, a lateral offset, avelocity, an acceleration, a curvature, or a curvature rate.

X: The system of any of paragraphs U-W, wherein the first weight isbased at least in part on one or more of: determining that a lateraldisplacement associated with a portion of the target trajectorycorresponding to the first portion of the reference trajectory is belowa first threshold distance for a given a time period; or determiningthat the lateral displacement associated with the portion of the targettrajectory corresponding to the first portion of the referencetrajectory is below a second threshold distance for a given distance.

Y: The system of any of paragraphs U-X, wherein the first reference costcomprises a penalty associated with generating the target trajectorythat deviates, at the first point and represented as the firstdifference, from the reference trajectory.

Z: A method comprising: receiving a first trajectory for an autonomousvehicle to traverse an environment; determining a first portion of thefirst trajectory; determining a second portion of the first trajectorythat is separate from the first portion; determining a first weightassociated with the first portion of the first trajectory; determining asecond weight associated with second portion of the first trajectory,wherein the first weight is different than the second weight;evaluating, with respect to the first trajectory and based at least inpart on the first weight and the second weight, a reference cost togenerate a second trajectory; and controlling the autonomous vehicle totraverse the environment based at least in part on the secondtrajectory.

AA: The method of paragraph Z, wherein evaluating the reference costcomprises: evaluating a first reference cost associated with the firstportion, the first reference cost proportional to the first weight; andevaluating a second reference cost associated with the second portion,the second reference cost proportional to the second weight.

AB: The method of paragraph Z or AA, wherein the first portion of thefirst trajectory is associated with points, and wherein the point is afirst point, the method further comprising: determining a point densityfor the points associated with the first portion of the first trajectorybased at least in part on a first cost associated with a curvature valueof the first portion or a second cost associated with a distance betweenthe first point of the first portion and a second point associated withan obstacle in the environment.

AC: The method of any of paragraphs Z-AB, wherein the reference cost isassociated with a difference between a first point on the firsttrajectory and a corresponding first point on the second trajectory.

AD: The method of paragraph AC, wherein the difference comprises one ormore of a difference in a yaw, a lateral offset, a velocity, anacceleration, a curvature, or a curvature rate.

AE: The method of any of paragraphs Z-AD, wherein the first portion ofthe first trajectory is associated with at least one of a discontinuityin the first trajectory or an obstacle cost that meets or exceeds athreshold obstacle cost.

AF. The method of paragraph AE, wherein: the discontinuity in the firsttrajectory represents a lane change action; or the obstacle cost meetingor exceeding the threshold obstacle cost represents a least a portion ofthe first trajectory passing within an area representing an obstacle inthe environment.

AG: The method of any of paragraphs Z-AF, further comprising:determining a drivable area through the environment for the autonomousvehicle, wherein the drivable area represents a region in theenvironment on which the autonomous vehicle is configured to traverseand an object in the environment; and evaluating an obstacle cost basedat least in part on the drivable area; wherein the second trajectory isbased at least in part on the obstacle cost.

AH: The method of paragraph AG, wherein determining the drivable area isbased at least in part on a velocity associated with the autonomousvehicle or a classification of the object.

AI: A non-transitory computer-readable medium storing instructions that,when executed, cause one or more processors to perform operationscomprising: receiving a first trajectory for an autonomous vehicle totraverse an environment; determining a first portion of the firsttrajectory; determining a second portion of the first trajectory that isseparate from the first portion; determining a first weight associatedwith the first portion of the first trajectory; determining a secondweight associated with second portion of the first trajectory, whereinthe first weight is different than the second weight; evaluating, withrespect to the first trajectory and based at least in part on the firstweight and the second weight, a reference cost to generate a secondtrajectory; and controlling the autonomous vehicle to traverse theenvironment based at least in part on the second trajectory.

AJ: The non-transitory computer-readable medium of paragraph AI,wherein: evaluating the reference cost comprises: evaluating a firstreference cost associated with the first portion, the first referencecost proportional to the first weight; and evaluating a second referencecost associated with the second portion, the second reference costproportional to the second weight.

AK: The non-transitory computer-readable medium of paragraph AI or AJ,wherein the reference cost is associated with a difference between afirst point on the first trajectory and a corresponding first point onthe second trajectory.

AL: The non-transitory computer-readable medium of any of paragraphsAI-AK, wherein the first portion of the first trajectory is associatedwith at least one of a discontinuity in the first trajectory or anobstacle cost that meets or exceeds a threshold obstacle cost.

AM: The non-transitory computer-readable medium of any of paragraphsAI-AL, wherein the operations further comprise: determining a drivablearea through the environment for the autonomous vehicle, wherein thedrivable area represents a region in the environment on which theautonomous vehicle is configured to traverse and an object in theenvironment; and evaluating an obstacle cost based at least in part onthe drivable area; wherein the second trajectory is based at least inpart on the obstacle cost.

AN: The non-transitory computer-readable medium of paragraph AM, whereindetermining the drivable area is based at least in part on a velocityassociated with the autonomous vehicle or a classification of theobject.

AO: A system comprising: one or more processors; and one or morecomputer-readable media storing computer-executable instructions that,when executed, cause the system to perform operations comprising:receiving sensor data from a sensor associated with an autonomousvehicle traversing an environment; determining a first drivable area forthe autonomous vehicle to traverse in the environment, the firstdrivable area associated with a first width; determining an objectrepresented in the sensor data; determining an object type associatedwith the object; determining, based at least in part on the object type,a distance threshold; determining a distance between the autonomousvehicle and the object; determining that the distance is below thedistance threshold; determining, based at least in part on the distancebeing below the distance threshold, a second drivable area associatedwith a second width that is less than the first width; determining acost associated with the second drivable area; determining, based atleast in part on the cost, a target trajectory; and controlling, basedat least in part on the target trajectory, the autonomous vehicle totraverse the environment.

AP: The system of paragraph AO, the operations further comprising:determining a probability that the object will be adjacent to theautonomous vehicle within a period of time; and determining that theprobability meets or exceeds a probability threshold; whereindetermining the second drivable area is further based at least in parton the probability meeting or exceeding the probability threshold.

AQ: The system of paragraph AO, wherein determining the second drivablearea is associated with a first time and comprises: determining, basedat least in part on a first velocity associated with the object and asecond velocity associated with the autonomous vehicle, a location ofthe autonomous vehicle at a second time after the first time; whereindetermining, the second drivable area is further based at least in parton the location.

AR: The system of paragraph AO, the operations further comprising:determining, based at least in part on a longitudinal displacement, atransition area between the first drivable area and the second drivablearea; wherein determining the target trajectory is further based atleast in part on the transition area.

AS: The system of paragraph AO, wherein the object type comprises one ormore of a bicyclist, a pedestrian, a vehicle, a static object, or adynamic object.

AT: A method comprising: receiving sensor data from a sensor associatedwith a vehicle traversing an environment; determining a first area forthe vehicle to traverse in the environment, the first area associatedwith a first width; determining an object represented in the sensordata; determining a distance between the vehicle and the object;determining, based at least in part on information associated with theobject, a second area associated with a second width that is less thanthe first width; determining, based at least in part on the second area,a cost associated with the vehicle traversing the second area;determining, based at least in part on the cost, a trajectory; andcontrolling the vehicle based at least in part on the trajectory.

AU: The method of paragraph AT, wherein the information comprises adistance of the object from the vehicle, the method further comprising:determining a classification associated with the object; anddetermining, based at least in part on the classification, a distancethreshold, wherein determining the second area is further based at leastin part on the distance being less than or equal to the distancethreshold.

AV: The method of paragraph AU, wherein the distance threshold is anupper distance threshold, the method further comprising: determining,based at least in part on the object, a lower distance threshold; anddetermining that the distance meets or exceeds the lower distancethreshold; wherein determining the second area is further based at leastin part on determining that the distance meets or exceeds the lowerdistance threshold.

AW: The method of paragraph AT, the method further comprising:determining a probability that the object will be within a distancethreshold to the vehicle within a period of time; and determining thatthe probability meets or exceeds a probability threshold; whereindetermining the second area is further based at least in part on theprobability meeting or exceeding the probability threshold.

AX: The method of paragraph AT, wherein determining the second area isassociated with a first time, the method further comprising:determining, based at least in part on a first velocity associated withthe object and a second velocity associated with the vehicle, a locationof the vehicle at a second time after the first time; whereindetermining the second area is further based at least in part on thelocation.

AY: The method of paragraph AT, wherein the vehicle is a first vehicle,the method further comprising: determining, based at least in part onthe sensor data, an object type associated with the object, the objecttype comprising at least one of: a second vehicle; a pedestrian; abicyclist; an animal; a static object; or a dynamic object.

AZ: The method of paragraph AY, wherein determining the second area isbased at least in part on the object type.

BA: The method of paragraph AT, further comprising: determining, basedat least in part on a longitudinal displacement associated with thevehicle, a transition area between the first area and the second area;wherein determining the trajectory is further based at least in part onthe transition area.

BB: A non-transitory computer-readable medium storing instructionsexecutable by one or more processors, wherein the instructions, whenexecuted, cause the one or more processors to perform operationscomprising: receiving sensor data from a sensor associated with avehicle traversing an environment; determining a first area on which thevehicle is able to traverse, the first area associated with a firstwidth; determining an object represented in the sensor data;determining, based at least in part on information associated with theobject, a second area associated with a second width; determining, basedat least in part on the second area, a cost associated with operatingthe vehicle in the environment; determining, based at least in part onthe cost, a trajectory associated with the vehicle; and controlling thevehicle based at least in part on the trajectory.

BC: The non-transitory computer-readable medium of paragraph BB, theoperations further comprising: determining a classification associatedwith the object determining a distance between the vehicle and theobject; and determining, based at least in part on the classification, adistance threshold, wherein the information comprises theclassification, and wherein determining the second area is further basedat least in part on the distance being less than or equal to thedistance threshold.

BD: The non-transitory computer-readable medium of paragraph BB, theoperations further comprising: determining a distance between thevehicle and the object; determining an upper distance threshold;determining, based at least in part on the object, a lower distancethreshold; and determining that the distance meets or exceeds the lowerdistance threshold, and wherein the information comprises determiningthat a distance between the object and the vehicle meets or exceeds thelower distance threshold.

BE: The non-transitory computer-readable medium of paragraph BB, theoperations further comprising: determining a probability that the objectwill be within a distance threshold to the vehicle within a period oftime; and determining that the probability meets or exceeds aprobability threshold, wherein the information comprises the probabilitymeeting or exceeding the probability threshold.

BF: The non-transitory computer-readable medium of paragraph BB, whereindetermining the second area is associated with a first time, theoperations further comprising: determining, based at least in part on afirst velocity associated with the object and a second velocityassociated with the vehicle, a location of the vehicle at a second timeafter the first time; wherein determining the second area is furtherbased at least in part on the location.

BG: The non-transitory computer-readable medium of paragraph BB, whereinthe vehicle is a first vehicle, the operations further comprising:determining, based at least in part on the sensor data, an object typeassociated with the object, wherein the object type indicates that theobject is associated with at least one of: a second vehicle; apedestrian; a bicyclist; an animal; a static object; or a dynamicobject.

BH: The non-transitory computer-readable medium of paragraph BB, theoperations further comprising: determining, based at least in part on alongitudinal displacement, a transition area between the first area andthe second area; wherein determining the trajectory is further based atleast in part on the transition area.

BI: A system comprising: one or more processors; and one or morecomputer-readable media storing computer-executable instructions that,when executed, cause the one or more processors to perform operationscomprising: receiving a vehicle trajectory associated with an autonomousvehicle traversing an environment; receiving sensor data from a sensorassociated with the autonomous vehicle; determining an objectrepresented in the sensor data; determining a drivable area on which theautonomous vehicle is to navigate; determining a location associatedwith a portion of the vehicle trajectory; determining, based at least inpart on the location, the drivable area, and a width associated with theautonomous vehicle, a cost; determining, based at least in part on thecost, an action of the autonomous vehicle associated with the location;and controlling the autonomous vehicle based at least in part on theaction.

BJ: The system of paragraph BI, wherein the action is a stop actionthat, when performed by the autonomous vehicle, causes the autonomousvehicle to stop within a threshold distance to the location.

BK: The system of paragraph BI, wherein the cost is based at least inpart on a first width of the drivable area at the location and a secondwidth of the autonomous vehicle.

BL: The system of paragraph BI, wherein determining the action isassociated with a first time, the operations further comprising:determining an object trajectory associated with the object, the objecttrajectory associated with increasing a distance between the object andthe location; wherein controlling the autonomous vehicle comprisesrefraining from performing the action based at least in part ondetermining the object trajectory.

BM: The system of paragraph BI, the operations further comprising:wherein determining the cost is further based at least in part on one ormore of an acceleration capability of the autonomous vehicle or aturning capability of the autonomous vehicle.

BN: The system of paragraph BI, the operations further comprising:determining that the object is a dynamic object; wherein the action is afollow action that, when performed by the autonomous vehicle, causes theautonomous vehicle to follow the object within a threshold distance.

BO: A method comprising: receiving a trajectory associated with avehicle traversing an environment; receiving sensor data from a sensorassociated with the vehicle; determining a drivable area; determining alocation associated with the trajectory; determining, based at least inpart on the trajectory and the drivable area, a cost associated with thelocation; determining an action of the vehicle associated with thelocation; and controlling, based at least in part on the action, thevehicle to traverse the environment.

BP: The method of paragraph BO, wherein the cost is based at least inpart on a first width of the drivable area at the location and a secondwidth of the vehicle.

BQ: The method of paragraph BO, further comprising: determining that thecost meets or exceeds a cost threshold, wherein the action is a stopaction that, when performed by the vehicle, causes the vehicle to stopwithin a second threshold distance to the location.

BR: The method of paragraph BO, wherein the cost is based at least inpart on a width of the drivable area at the location and a width of thevehicle.

BS: The method of paragraph BO, further comprising: determining, basedat least in part on the sensor data, an object in the environment; anddetermining an object trajectory associated with the object, the objecttrajectory associated with increasing a distance between the object andthe location, wherein the action comprises continuing along thetrajectory.

BT: The method of paragraph BO, further comprising: determining, basedat least in part on the sensor data, an object in the environment; anddetermining an object type associated with the object; whereindetermining the cost is further based at least in part on the objecttype.

BU: The method of paragraph BO, further comprising: determining, basedat least in part on the sensor data, an object in the environment; anddetermining that the object is a dynamic object; wherein the action is afollow action that, when performed by the vehicle, causes the vehicle tofollow the object within a second threshold distance.

BV: A non-transitory computer-readable medium storing instructionsexecutable by a processor, wherein the instructions, when executed,cause the processor to perform operations comprising: receiving atrajectory associated with a vehicle traversing an environment;receiving sensor data from a sensor associated with the vehicle;determining an area; determining a location associated with a portion ofthe trajectory; determining, based at least in part on the trajectory, acost associated with the location; determining an action of the vehicleassociated with the location; and controlling, based at least in part onthe action, the vehicle to traverse the environment.

BW: The non-transitory computer-readable medium of paragraph BV, whereina first boundary of the area is associated with a first cost function,and wherein a second boundary of the area is associated with a secondcost function different from the first cost function.

BX: The non-transitory computer-readable medium of paragraph BV, theoperations further comprising determining the cost meets or exceeds athreshold cost, and wherein the action is a stop action that, whenperformed by the vehicle, causes the vehicle to stop within a seconddistance to the location.

BY: The non-transitory computer-readable medium of paragraph BV, whereinthe cost is based at least in part on a first width of the area at thelocation and a second width of the vehicle.

BZ: The non-transitory computer-readable medium of paragraph BV, theoperations further comprising: determining, based at least in part onthe sensor data, an object in the environment; and determining an objecttrajectory associated with the object, the object trajectory associatedwith increasing a distance between the object and the location, whereinthe action comprises continuing along the trajectory.

CA: The non-transitory computer-readable medium of paragraph BV, theoperations further comprising: determining, based at least in part onthe sensor data, an object in the environment; determining an objecttype associated with the object; determining a first pose associatedwith the vehicle; and determining a second pose associated with theobject, wherein a capability of the vehicle to traverse the location isbased at least in part on the first pose, wherein the area is based atleast in part on the second pose, and wherein determining the cost isfurther based at least in part on the object type, the capability of thevehicle, and the area.

CB: The non-transitory computer-readable medium of paragraph BV, theoperations further comprising: determining, based at least in part onthe sensor data, an object in the environment; determining that theobject is a dynamic object; wherein the action is a follow action that,when performed by the vehicle, causes the vehicle to follow the objectwithin a second threshold distance.

While the example clauses described above are described with respect toone particular implementation, it should be understood that, in thecontext of this document, the content of the example clauses can also beimplemented via a method, device, system, a computer-readable medium,and/or another implementation. Additionally, any of examples A-CB may beimplemented alone or in combination with any other one or more of theexamples A-CB.

CONCLUSION

While one or more examples of the techniques described herein have beendescribed, various alterations, additions, permutations and equivalentsthereof are included within the scope of the techniques describedherein.

In the description of examples, reference is made to the accompanyingdrawings that form a part hereof, which show by way of illustrationspecific examples of the claimed subject matter. It is to be understoodthat other examples can be used and that changes or alterations, such asstructural changes, can be made. Such examples, changes or alterationsare not necessarily departures from the scope with respect to theintended claimed subject matter. While the steps herein can be presentedin a certain order, in some cases the ordering can be changed so thatcertain inputs are provided at different times or in a different orderwithout changing the function of the systems and methods described. Thedisclosed procedures could also be executed in different orders.Additionally, various computations that are herein need not be performedin the order disclosed, and other examples using alternative orderingsof the computations could be readily implemented. In addition to beingreordered, the computations could also be decomposed intosub-computations with the same results.

What is claimed is:
 1. A system comprising: one or more processors; andone or more computer-readable media storing computer-executableinstructions that, when executed, cause the system to perform operationscomprising: receiving sensor data from a sensor associated with anautonomous vehicle traversing an environment; determining a firstdrivable area for the autonomous vehicle to traverse in the environment,the first drivable area associated with a first width; determining anobject represented in the sensor data; determining an object typeassociated with the object; determining, based at least in part on theobject type, a distance threshold of a plurality of distance thresholds,wherein the distance threshold is based at least in part on an objecttype; determining a distance between the autonomous vehicle and theobject; determining that the distance is below the distance threshold;determining, based at least in part on the distance being below thedistance threshold, a second drivable area associated with a secondwidth that is less than the first width; determining a cost associatedwith the second drivable area; determining, based at least in part onthe cost, a target trajectory; and controlling, based at least in parton the target trajectory, the autonomous vehicle to traverse theenvironment.
 2. The system of claim 1, the operations furthercomprising: determining a probability that the object will be adjacentto the autonomous vehicle within a period of time; and determining thatthe probability meets or exceeds a probability threshold; whereindetermining the second drivable area is further based at least in parton the probability meeting or exceeding the probability threshold. 3.The system of claim 1, wherein determining the second drivable area isassociated with a first time and comprises: determining, based at leastin part on a first velocity associated with the object and a secondvelocity associated with the autonomous vehicle, a location of theautonomous vehicle at a second time after the first time; whereindetermining, the second drivable area is further based at least in parton the location.
 4. The system of claim 1, the operations furthercomprising: determining, based at least in part on a longitudinaldisplacement, a transition area between the first drivable area and thesecond drivable area; wherein determining the target trajectory isfurther based at least in part on the transition area.
 5. The system ofclaim 1, wherein the object type comprises one or more of a bicyclist, apedestrian, a vehicle, a static object, or a dynamic object.
 6. A methodcomprising: receiving sensor data from a sensor associated with avehicle traversing an environment; determining a first area for thevehicle to traverse in the environment, the first area associated with afirst width; determining an object represented in the sensor data;determining a distance between the vehicle and the object; determining aclassification associated with the object; determining, based at leastin part on the classification, a width of a lane bias region of aplurality of widths, wherein the width is based at least in part on aclassification; determining, based at least in part on theclassification, a second area associated with a second width that isless than the first width by the width of the lane bias region;determining, based at least in part on the second area, a costassociated with the vehicle traversing the second area; determining,based at least in part on the cost, a trajectory; and controlling thevehicle based at least in part on the trajectory.
 7. The method of claim6, further comprising: determining a distance between the object and thevehicle; and determining, based at least in part on the classification,a distance threshold, wherein determining the second area is furtherbased at least in part on the distance being less than or equal to thedistance threshold.
 8. The method of claim 7, wherein the distancethreshold is an upper distance threshold, the method further comprising:determining, based at least in part on the object, a lower distancethreshold; and determining that the distance meets or exceeds the lowerdistance threshold; wherein determining the second area is further basedat least in part on determining that the distance meets or exceeds thelower distance threshold.
 9. The method of claim 6, the method furthercomprising: determining a probability that the object will be within adistance threshold to the vehicle within a period of time; anddetermining that the probability meets or exceeds a probabilitythreshold; wherein determining the second area is further based at leastin part on the probability meeting or exceeding the probabilitythreshold.
 10. The method of claim 6, wherein determining the secondarea is associated with a first time, the method further comprising:determining, based at least in part on a first velocity associated withthe object and a second velocity associated with the vehicle, a locationof the vehicle at a second time after the first time; whereindetermining the second area is further based at least in part on thelocation.
 11. The method of claim 6, wherein the vehicle is a firstvehicle, and the classification comprises at least one of: a secondvehicle; a pedestrian; a bicyclist; an animal; a static object; or adynamic object.
 12. The method of claim 6, wherein determining theclassification is based at least in part on the sensor data.
 13. Themethod of claim 6, further comprising: determining, based at least inpart on a longitudinal displacement associated with the vehicle, atransition area between the first area and the second area; whereindetermining the trajectory is further based at least in part on thetransition area.
 14. A non-transitory computer-readable medium storinginstructions executable by one or more processors, wherein theinstructions, when executed, cause the one or more processors to performoperations comprising: receiving sensor data from a sensor associatedwith a vehicle traversing an environment; determining a first area onwhich the vehicle is able to traverse, the first area associated with afirst width; determining an object represented in the sensor data;determining a classification associated with the object; determining,based at least in part on the classification, a second area associatedwith a second width; determining, based at least in part on the secondarea, a cost associated with operating the vehicle in the environment;determining, based at least in part on the cost, a trajectory associatedwith the vehicle; and controlling the vehicle based at least in part onthe trajectory.
 15. The non-transitory computer-readable medium of claim14, the operations further comprising: determining a distance betweenthe vehicle and the object; and determining, based at least in part onthe classification, a distance threshold, wherein determining the secondarea is further based at least in part on the distance being less thanor equal to the distance threshold.
 16. The non-transitorycomputer-readable medium of claim 14, the operations further comprising:determining a distance between the vehicle and the object; determiningan upper distance threshold; determining, based at least in part on theobject, a lower distance threshold; determining that the distance meetsor exceeds the lower distance threshold; and determining the second areafurther based at least in part on the distance between the vehicle andthe object meeting or exceeding the lower distance threshold.
 17. Thenon-transitory computer-readable medium of claim 14, the operationsfurther comprising: determining a probability that the object will bewithin a distance threshold to the vehicle within a period of time;determining that the probability meets or exceeds a probabilitythreshold; and determining the second area further based at least inpart on the probability meeting or exceeding the probability threshold.18. The non-transitory computer-readable medium of claim 14, whereindetermining the second area is associated with a first time, theoperations further comprising: determining, based at least in part on afirst velocity associated with the object and a second velocityassociated with the vehicle, a location of the vehicle at a second timeafter the first time; wherein determining the second area is furtherbased at least in part on the location.
 19. The non-transitorycomputer-readable medium of claim 14, wherein the vehicle is a firstvehicle, and the classification indicates that the object is associatedwith at least one of: a second vehicle; a pedestrian; a bicyclist; ananimal; a static object; or a dynamic object.
 20. The non-transitorycomputer-readable medium of claim 14, the operations further comprising:determining, based at least in part on a longitudinal displacement, atransition area between the first area and the second area; whereindetermining the trajectory is further based at least in part on thetransition area.